Category Archives: Image Technologies

Face Recognition Tests

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Referring to my recent post about Face Recognition Systems, I did some trials with my “About” photo. Here are the results of my Face Recognition Tests :

Animetrics

Face Detection Tests : Animetrics

Face Detection Tests : Animetrics

{"images": [
{"time": 4.328,
"status": "Complete",
"url": "http://www.web3.lu/download/Marco_Barnig_529x529.jpg",
"width": 529,
"height": 529,
"setpose_image": "http://api.animetrics.com/img/setpose/d89864cc3aaab341d4211113a8310f9a.jpg",
"faces": [
{"topLeftX": 206,
"topLeftY": 112,
"width": 82,
"height": 82,
"leftEyeCenterX": 227.525,
"leftEyeCenterY": 126.692,
"rightEyeCenterX": 272.967,
"rightEyeCenterY": 128.742,
"noseTipX": 252.159,
"noseTipY": 158.973,
"noseBtwEyesX": 251.711,
"noseBtwEyesY": 126.492,
"chinTipX": -1,
"chinTipY": -1,
"leftEyeCornerLeftX": 219.005,
"leftEyeCornerLeftY": 126.308,
"leftEyeCornerRightX": 237.433,
"leftEyeCornerRightY": 127.85,
"rightEyeCornerLeftX": 262.995,
"rightEyeCornerLeftY": 129.004,
"rightEyeCornerRightX": 280.777,
"rightEyeCornerRightY": 129.094,
"rightEarTragusX": -1,
"rightEarTragusY": -1,
"leftEarTragusX": -1,
"leftEarTragusY": -1,
"leftEyeBrowLeftX": 211.478,
"leftEyeBrowLeftY": 120.93,
"leftEyeBrowMiddleX": 226.005,
"leftEyeBrowMiddleY": 117.767,
"leftEyeBrowRightX": 241.796,
"leftEyeBrowRightY": 120.416,
"rightEyeBrowLeftX": 264.142,
"rightEyeBrowLeftY": 121.101,
"rightEyeBrowMiddleX": 278.625,
"rightEyeBrowMiddleY": 119.38,
"rightEyeBrowRightX": 290.026,
"rightEyeBrowRightY": 124.059,
"nostrilLeftHoleBottomX": 243.92,
"nostrilLeftHoleBottomY": 168.822,
"nostrilRightHoleBottomX": 257.572,
"nostrilRightHoleBottomY": 170.683,
"nostrilLeftSideX": 236.867,
"nostrilLeftSideY": 163.555,
"nostrilRightSideX": 262.073,
"nostrilRightSideY": 165.049,
"lipCornerLeftX": -1,
"lipCornerLeftY": -1,
"lipLineMiddleX": -1,
"lipLineMiddleY": -1,
"lipCornerRightX": -1,
"lipCornerRightY": -1,
"pitch": -6.52624,
"yaw": -6.43,
"roll": 2.35988
}]}]}

APICloudMe

Face Recognition Tests : APICloudMe FaceRect and FaceMark

Face Recognition Tests : APICloudMe FaceRect and FaceMark

{"faces" : [
{"orientation" : "frontal",
"landmarks" : [
{"x" : 193,"y" : 125},
{"x" : 191,"y" : 145},
{"x" : 192,"y" : 163},
{"x" : 196,"y" : 178},
{"x" : 206,"y" : 194},
{"x" : 218,"y" : 204},
{"x" : 229,"y" : 206},
{"x" : 243,"y" : 209},
{"x" : 259,"y" : 206},
{"x" : 268,"y" : 202},
{"x" : 278,"y" : 195},
{"x" : 287,"y" : 182},
{"x" : 292,"y" : 167},
{"x" : 296,"y" : 150},
{"x" : 297,"y" : 129},
{"x" : 284,"y" : 112},
{"x" : 279,"y" : 108},
{"x" : 268,"y" : 110},
{"x" : 263,"y" : 116},
{"x" : 270,"y" : 113},
{"x" : 277,"y" : 111},
{"x" : 214,"y" : 111},
{"x" : 223,"y" : 107},
{"x" : 234,"y" : 110},
{"x" : 238,"y" : 115},
{"x" : 232,"y" : 113},
{"x" : 223,"y" : 110},
{"x" : 217,"y" : 127},
{"x" : 228,"y" : 121},
{"x" : 236,"y" : 129},
{"x" : 227,"y" : 131},
{"x" : 227,"y" : 126},
{"x" : 280,"y" : 129},
{"x" : 271,"y" : 123},
{"x" : 262,"y" : 130},
{"x" : 271,"y" : 133},
{"x" : 271,"y" : 127},
{"x" : 242,"y" : 128},
{"x" : 238,"y" : 145},
{"x" : 232,"y" : 157},
{"x" : 232,"y" : 163},
{"x" : 247,"y" : 168},
{"x" : 262,"y" : 164},
{"x" : 262,"y" : 158},
{"x" : 258,"y" : 146},
{"x" : 256,"y" : 129},
{"x" : 239,"y" : 163},
{"x" : 256,"y" : 164},
{"x" : 221,"y" : 179},
{"x" : 232,"y" : 178},
{"x" : 240,"y" : 179},
{"x" : 245,"y" : 180},
{"x" : 251,"y" : 180},
{"x" : 259,"y" : 180},
{"x" : 269,"y" : 182},
{"x" : 261,"y" : 186},
{"x" : 253,"y" : 189},
{"x" : 245,"y" : 189},
{"x" : 236,"y" : 187},
{"x" : 229,"y" : 184},
{"x" : 235,"y" : 182},
{"x" : 245,"y" : 184},
{"x" : 255,"y" : 184},
{"x" : 254,"y" : 183},
{"x" : 245,"y" : 183},
{"x" : 235,"y" : 182},
{"x" : 245,"y" : 183},
{"x" : 249,"y" : 160}
]}],
"image" : {
"width" : 529,
"height" : 529
}}

Betaface API

Image ID : 65fe585d-e565-496e-ab43-bcfdc18c7918
Faces : 2

Face detection Test : Betaface API

Face Recognition Tests : Betaface API

hair color type: red (24%), gender: male (52%), age: 49 (14%), ethnicity: white (57%), smile: yes (15%), glasses: yes (48%), mustache: yes (46%), beard: yes (35%)
500: HEAD Height/Width level parameter (POS = long narrow face NEG = short wide face)(min -2 max 2): 1
501: HEAD TopWidth/BottomWidth level parameter (POS = heart shape NEG = rectangular face)(min -2 max 2):0
502: NOSE Height/Width level parameter (NEG = thinner) (min -2 max 2) : 2
503: NOSE TopWidth/BottomWidth level parameter (NEG = wider at the bottom) (min -2 max 2) : 1
504: MOUTH Width level parameter (min -2 max 2) : 1
505: MOUTH Height level parameter (NEG = thin) (min -2 max 2) : 1
521: MOUTH Corners vertical offset level parameter (NEG = higher) (min -2 max 2) : -2
506: EYES Height/Width level parameter (NEG = thinner and wider, POS = more round) (min -2 max 2) : -1
507: EYES Angle level parameter (NEG = inner eye corners moved towards mouth) (min -2 max 2) : 1
517: EYES closeness level parameter (NEG = closer) (min -2 max 2) : 0
518: EYES vertical position level parameter (NEG = higher) (min -2 max 2) : 0
508: HAIRSTYLE Sides thickness level parameter (min 0 max 3) : 0
509: HAIRSTYLE Hair length level parameter (min 0 max 5) : 0
510: HAIRSTYLE Forehead hair presence parameter (min 0 max 1) : 1
511: HAIRSTYLE Hair Top hair amount level parameter (min 0 max 4) : 3
512: FACE HAIR Mustache level parameter (min 0 max 2) : 0
513: FACE HAIR Beard level parameter (min 0 max 2) : 0
514: GLASSES presence level parameter (min 0 max 1) : 0
515: EYEBROWS thickness level parameter (min -2 max 2) : -2
516: EYEBROWS vertical pos level parameter (POS = closer to the eyes) (min -2 max 2) : -2
520: EYEBROWS Angle level parameter(NEG = inner eyebrows corners moved towards mouth)(min -2 max 2) :-2
519: TEETH presence level parameter (min 0 max 1) : 1
522: NOSE-CHIN distance level parameter (min -2 max 2) : 0
620756992: face height/face width ratio / avg height/width ratio : 1.0478431040781575
620822528: face chin width/face width ratio / avg height/width ratio : 1.0038425243863847
620888064: face current eyes distance/ avg eyes distance ratio : 1.0104771666577224
620953600: eyes vertical position - avg position, minus - higher : -0.00089347261759175321
621019136: distance between chin bottom and low lip / avg distance : 0.97106500562603393
621084672: distance between nose bottom and top lip / avg distance : 1.0075242288018134
621150208: distance between nose top and bottom / avg distance : 1.0619860919447868
621215744: distance between nose left and right / avg distance : 1.0426301239394231
621281280: distance between left mouth corner and right mouth corner / avg distance : 1.0806991515139102
621346816: eyebrows thichkness / avg thichkness : 0.83331489266473235
621412352: ratio (low nose part width / top nose part width) / avg ratio : 0.9717897529241869
621477888: eye height/width ratio / avg height/width ratio : 0.9611420163590253
621543424: width of the chin / avg width of the chin : 0.96738062415147075
621608960: angle of the eyes in degrees - avg angle. Negative angle mean inner eye corners moved towards mouth from average position : -0.35247882153940435
621674496: distance between eyebrows and eyes / avg distance : 0.88418599076781756
621740032: face width / avg width ratio : 0.96367766920692888
621805568: skin color (Weight) (min 0 max 1) : 1.340999960899353
621871104: skin color (H) (min 0 max 180) : 7
621936640: skin color (S) (min 0 max 255) : 81
622002176: skin color (V) (min 0 max 255) : 208
622067712: skin color (R) (min 0 max 255) : 208
622133248: skin color (G) (min 0 max 255) : 157
622198784: skin color (B) (min 0 max 255) : 142
622264320: mustache color if detected (Weight) (min 0 max 1) : 0
622329856: mustache color if detected (H) (min 0 max 180) : 0
622395392: mustache color if detected (S) (min 0 max 255) : 0
622460928: mustache color if detected (V) (min 0 max 255) : 0
622526464: mustache color if detected (R) (min 0 max 255) : 0
622592000: mustache color if detected (G) (min 0 max 255) : 0
622657536: mustache color if detected (B) (min 0 max 255) : 0
622723072: beard color if detected (Weight) (min 0 max 1) : 0
622788608: beard color if detected (H) (min 0 max 180) : 0
622854144: beard color if detected (S) (min 0 max 255) : 0
622919680: beard color if detected (V) (min 0 max 255) : 0
622985216: beard color if detected (R) (min 0 max 255) : 0
623050752: beard color if detected (G) (min 0 max 255) : 0
623116288: beard color if detected (B) (min 0 max 255) : 0
623181824: weight of teeth color (Weight) (min 0 max 1) : 0.4440000057220459
623247360: glasses detection (weight floating value, related to thickness of rim/confidence) (min 0.03 max 1) : 0.065934065934065936
623312896: color of the hair area (Weight) (min 0 max 1) : 0.23899999260902405
623378432: color of the hair area (H) (min 0 max 180) : 4
623443968: color of the hair area (S) (min 0 max 255) : 151
623509504: color of the hair area (V) (min 0 max 255) : 130
623575040: color of the hair area (R) (min 0 max 255) : 130
623640576: color of the hair area (G) (min 0 max 255) : 63
623706112: color of the hair area (B) (min 0 max 255) : 53
673513472: eyebrows angle. Negative angle mean inner eyebrow corners moved towards mouth from average position : 0.086002873281683989
673579008: mouth corners Y offset - avg offset : -0.12499242147802289
673644544: mouth height / avg height : 1.1755344432588537
673710080: nose tip to chin distance / avg distance : 1.0093704038280917

BioID

Face Recognition Tests : BioID

Face Recognition Tests : BioID

<?xml version="1.0" encoding="utf-16"?>
<OperationResults xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://schemas.bioid.com/2012/02/BWSMessages">
  <JobID>955410d9-eb2c-43db-b3ca-deeedcd665af</JobID>
  <Command>QualityCheck</Command>
  <Succeeded>true</Succeeded>
  <Samples>
    <Sample Trait="Face" Suitable="true">
      <Errors>
        <Error>
          <Code>ImageTooSmall</Code>
          <Message>The part of the image containing the found face is too small.</Message>
          <Details>The found face (with an eye-distance of 43 pixels) does not have the required eye-distance of at least 240 pixels.</Details>
        </Error>
        <Error>
          <Code>ImageTooSmall</Code>
          <Message>The part of the image containing the found face is too small.</Message>
          <Details>The cropped face image (with 146 x 188 pixels) does not have the minimum expected resolution of 827 x 1063 pixels.</Details>
        </Error>
        <Error>
          <Code>FaceAsymmetry</Code>
          <Message>It seems that the face of the found person is somehow asymmetric, maybe due to bad illumination and/or due to a wrong pose.</Message>
          <Details>An asymmetry of 88.20 was calculated, where only a value up to 50.00 is allowed.</Details>
        </Error>
        <Error>
          <Code>MissingTimeStamp</Code>
          <Message>The image does not have any tag attached which could be used to find out when it was taken. It cannot be assured that the image is not older than 183 days.</Message>
          <Details />
        </Error>
        <Error>
          <Code>ImageOverExposure</Code>
          <Message>The image is over-exposed, i.e. it has too many very light pixels.</Message>
          <Details>The amount of very bright pixels is 1.34%, where only 1.00% are allowed.</Details>
        </Error>
      </Errors>
      <Tags>
        <RightEye X="52.174" Y="84.108" />
        <LeftEye X="95.448" Y="86.173" />
      </Tags>
    </Sample>
  </Samples>
  <Statistics>
    <ProcessingTime>00:00:01.3642947</ProcessingTime>
    <TotalServiceTime>00:00:01.6941376</TotalServiceTime>
  </Statistics>
</OperationResults>

BiometryCloud

No demo app available.

HP Labs Multimedia Analytical Platform

Face Recognition Tests : HP Labs Multimedia

Face Recognition Tests : HP Labs Multimedia Analytical Platform

{
"pic":{
"id_pic":"8f2cb88987e9e6b88813c5c17599204a25a8d63b",
"height":"529",
"width":"529"
},
"face":[{
"id_face":"2500002",
"id_pic":"8f2cb88987e9e6b88813c5c17599204a25a8d63b",
"bb_left":"209",
"bb_top":"109",
"bb_right":"290",
"bb_bottom":"190"
}]}

Lambda Labs Face

Face Recognition Tests : Lambda Labs

Face Recognition Tests : Lambda Labs

{
"status": "success",
"images": ["http://www.web3.lu/download/Marco_Barnig_529x529.jpg"],
"photos": [{"url": "http://www.web3.lu/download/Marco_Barnig_529x529.jpg",
"width": 529,
"tags": [
{"eye_left": {"y": 128,"x": 269},
"confidence": 0.978945010372561,
"center": {"y": 143,"x": 250},
"mouth_right": {"y": 180,"x": 267},
"mouth_left": {"y": 180,"x": 220},
"height": 128,"width": 128,
"mouth_center": {"y": 180,"x": 243.5},
"nose": {"y": 166,"x": 250},
"eye_right": {"y": 129,"x": 231},
"tid": "31337",
"attributes": [{"smile_rating": 0.5,"smiling": false,"confidence": 0.5},
{"gender": "male","confidence": 0.6564017215167235}],
"uids": [
{"confidence": 0.71,"prediction": "TigerWoods","uid": "TigerWoods@CELEBS"},
{"confidence": 0.258,"prediction": "ArnoldS","uid": "ArnoldS@CELEBS"}]}],
"height": 529
}]}

Orbeus ReKognition

Face Recognition Tests : Orbeus ReKognition

Face Recognition Tests : Orbeus ReKognition

{
"url" : "base64_ZNlBfC.jpg",
"face_detection" : [{
"boundingbox" : {
"tl" : {"x" : "188.46","y" : "80.77"},
"size" : {"width" : "126.15","height" : "126.15"}},
"confidence" : "0.98",
"name" : "mark_zuckerberg:0.37,obama:0.33,brad_pitt:0.16,",
"matches" : [{"tag" : "mark_zuckerberg","score" : "0.37"},
{"tag" : "obama","score" : "0.33"},
{"tag" : "brad_pitt","score" : "0.16"}],
"eye_left" : {"x" : "229.6","y" : "125.6"},
"eye_right" : {"x" : "272.5","y" : "127.7"},
"nose" : {"x" : "252.4","y" : "161.5"},
"mouth l" : {"x" : "226.9","y" : "173"},
"mouth_l" : {"x" : "226.9","y" : "173"},
"mouth r" : {"x" : "266.1","y" : "175.6"},
"mouth_r" : {"x" : "266.1","y" : "175.6"},
"pose" : {"roll" : "1.68","yaw" : "19.83","pitch" : "-11.7"},
"b_ll" : {"x" : "211.6","y" : "118"},
"b_lm" : {"x" : "226.7","y" : "113.2"},
"b_lr" : {"x" : "242.2","y" : "116.1"},
"b_rl" : {"x" : "263.5","y" : "117.1",
"b_rm" : {"x" : "277.8","y" : "115.5"},
"b_rr" : {"x" : "290.5","y" : "120"},
"e_ll" : {"x" : "221.5","y" : "125.7"},
"e_lr" : {"x" : "237.4","y" : "126.7"},
"e_lu" : {"x" : "229.9","y" : "122.5"},
"e_ld" : {"x" : "229.4","y" : "128.1"},
"e_rl" : {"x" : "265.3","y" : "128.2"},
"e_rr" : {"x" : "279.5","y" : "128.4"},
"e_ru" : {"x" : "272.5","y" : "124.8"},
"e_rd" : {"x" : "272.5","y" : "130.1"},
"n_l" : {"x" : "240.3","y" : "161.2"},
"n_r" : {"x" : "259.7","y" : "163.8"},
"m_u" : {"x" : "248.2","y" : "174.2"},
"m_d" : {"x" : "246.3","y" : "189.3"},
"race" : {"white" : "0.56"},
"age" : "48.28",
"glasses" : "1",
"eye_closed" : "0",
"mouth_open_wide" : "0.77",
"sex" : "0.96"},
{"boundingbox" : {
"tl" : {"x" : "19.23","y" : "221.54"},
"size" : {"width" : "160","height" : "160"}},
"confidence" : "0.07",
"name" : "obama:0.03,brad_pitt:0.02,jim_parsons:0.01,",
"matches" : [
{"tag" : "obama","score" : "0.03"},
{"tag" : "brad_pitt","score" : "0.02"},
{"tag" : "jim_parsons","score" : "0.01"}
],
"eye_left" : {"x" : "93.7","y" : "257.9"},
"eye_right" : {"x" : "128.4","y" : "309.1"},
"nose" : {"x" : "95.8","y" : "299.5"},
"mouth l" : {"x" : "58.9","y" : "306.9"},
"mouth_l" : {"x" : "58.9","y" : "306.9"},
"mouth r" : {"x" : "94.1","y" : "350.9"},
"mouth_r" : {"x" : "94.1","y" : "350.9"},
"pose" : {"roll" : "59.26","yaw" : "-11.22","pitch" : "9.96"},
"b_ll" : {"x" : "102.4","y" : "227.3"},
"b_lm" : {"x" : "114.9","y" : "240.8"},
"b_lr" : {"x" : "119.5","y" : "259.5"},
"b_rl" : {"x" : "133.9","y" : "282.1"},
"b_rm" : {"x" : "147.7","y" : "295.7"},
"b_rr" : {"x" : "153.8","y" : "312.3"},
"e_ll" : {"x" : "88.2","y" : "248.3"},
"e_lr" : {"x" : "100.2","y" : "267.6"},
"e_lu" : {"x" : "94.2","y" : "257.4"},
"e_ld" : {"x" : "92.7","y" : "258.4"},
"e_rl" : {"x" : "122.3","y" : "299.1"},
"e_rr" : {"x" : "134.7","y" : "319.7"},
"e_ru" : {"x" : "129.5","y" : "308.5"},
"e_rd" : {"x" : "127.2","y" : "309.6"},
"n_l" : {"x" : "78.7","y" : "298.6"},
"n_r" : {"x" : "97.8","y" : "318.4"},
"m_u" : {"x" : "80.4","y" : "321.2"},
"m_d" : {"x" : "72.6","y" : "328.5"},
"race" : {"black" : "0.63"},
"age" : "23.07",
"glasses" : "0.98",
"eye_closed" : "0.9",
"mouth_open_wide" : "0.46",
"sex" : "0.66"
}],
"ori_img_size" : {
"width" : "529",
"height" : "529"
},
"usage" : {
"quota" : "-10261829",
"status" : "Succeed.",
"api_id" : "4321"
}
}

Sky Biometry

Face Recognition Tests : SkyBiometry

Face Recognition Tests : SkyBiometry

face: (85%)
gender: male (86%)
smiling: true (100%)
glasses: true (57%)
dark glasses: false (21%)
eyes: open (80%)
mood: angry (69%)
N: 0%
A: 69%
D: 40%
F: 0%
H: 23%
S: 0%
SP: 21%
roll: 3
yaw: -14

HDR : high-dynamic-range imaging

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Last update : December 25, 2021

HDR (High-dynamic-range imaging) is a set of methods used in imaging and photography to capture a greater dynamic range between the lightest and darkest areas of an image than current standard digital imaging methods or photographic methods. The two main sources of high-dynamic-range images are computer renderings and merging of multiple standard-dynamic-range (SDR) photographs created with exposure bracketing.

As the popularity of the HDR imaging method increased in the last years, several camera manufactures are now offering built-in high-dynamic-range features. HDR is also integrated in new smartphones. Since iOS4.1, Apple iPhones have a built-in HDR functionality. Android launched HDR mode for the camera app in version 4.2 (Jelly Bean); Blackberry introduced HDR in the Z10 with OS update 10.1.

hdr

HDR Photo by Jon Rutlen on Flickr

A very detailed contribution about concepts, standards and related aspects of HDR has been published by Digiarty Software Inc. The following links provide some ancient information about HDR :

JPEG Chroma Subsampling

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The JPEG compressed file format can produce significant reductions in file size through lossy compression. The compression techniques take advantage of the limitations of the human eye by discarding additional image details that may not be as noticeable to the human observer.

Humans are much more sensitive to changes in luminance (brightness) than  to chrominance (color) differences. JPEG can discard a lot more color information than luminance in the compression process. Chroma subsampling is the process whereby the color information in the image is sampled at a lower resolution than the original. JPEG translates 8-bit RGB data (Red, Green, Blue) into 8-bit YCbCr data (Luminance, Chroma Blue, Chroma Red).

The different levels of YCbCr subsampling are :

  • 4:4:4 – The resolution of chrominance information is preserved at the same rate as the luminance information. (1×1, subsampling disabled)
  • 4:2:2 – Half of the horizontal resolution in the chrominance is dropped, while the full resolution is retained in the vertical direction, with respect to the luminance. (2×1 chroma subsampling)
  • 4:1:1 – Only a quarter of the chrominance information is preserved in the horizontal direction with respect to the luminance information
  • 4:2:0 – With respect to the information in the luminance channel, the chrominance resolution in both the horizontal and vertical directions is cut in half (2×2 chroma subsampling)

JPEG chroma subsampling is not a particularly good mechanism for compressing images used in the medical field where the chrominance may be equally as important as the luminance.

Photoshop uses different chroma subsampling levels depending on the Quality settings:

  • 2×2 Chroma Subsampling – Save Quality 0-6 or Save For Web Quality 0-50
  • No Chroma Subsampling – Save As Quality 7-12  or Save For Web Quality 51-100

Additional informations about JPEG Chroma subsampling are available at the following links :

Metadata handled by Synology Photostation

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Last update : September 17, 2013

The following metadata for images are handled by the Photostation application of the Synology Diskstation :

—- EXIF —-

  • Make (IFD0)
  • Camera Model Name (IFD0)
  • Exposure Time (ExifIFD)
  • F Number (ExifIFD)
  • ISO (ExifIFD)
  • Exif Version (ExifIFD)
  • Date/Time Original (ExifIFD) : yyyy:mm:dd hh:mm:ss
  • GPS Version ID (GPS)
  • GPS Latitude Ref (GPS)
  • GPS Latitude (GPS)
  • GPS Longitude Ref (GPS)
  • GPS Longitude (GPS)

—- XMP —-

  • XMP Toolkit (XMP-x)
  • Region Person Display Name (XMP-MP)
  • Region Rectangle (XMP-MP) : x1, y1, x2, y2
  • Description (XMP-dc)
  • Subject (XMP-dc) : keywords

XMP-exif metadata tags are not recognised.

Optimize favicons

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Last update : March 21, 2016

Favicons plugin by Telegraphics

Favicons plugin by Telegraphics

Favicons (.ico files) are the little pictures at the left of the URL on the address bar of the browser. Favicons are an excellent free branding tool for webmasters and blog owners. They help you create brand awareness. Favicons are downloaded by each new visitor to a website. Favicons are extremely important because they are requested before any other components by the browser. By optimizing faveicons, you reduce bandwidth costs and server load.

With the code

<link rel="shortcut icon" href="chateau.ico" />

you can reference any location for the favicon. If it is absent, the Browser tries to fetch it from the domains root instead and each time the browser requests this file, the cookies for the server’s root are sent.

An outstanding tutorial how to include favicons on websites and especially in WordPress blogs is shown at MaxBlogPress. There are various free tools available to create favicons. A plugin for Photoshop to create and optimize favicons is available from Telegraphics, free software by Toby Tain.

Thera are also numerous online webtools available to create and optimize favicons, for example :

Some useful links to websites with more information about optimizing favicons are listed below :

Optimize png images

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PNG (Portable Network Graphics) is a bitmapped image format that employs lossless data compression. PNG was created to improve upon and replace GIF (Graphics Interchange Format) as an image-file format not requiring a patent license.

PNG supports palette-based images, grayscale images, and full-color non-palette-based RGB[A] images (with or without alpha channel). PNG uses a non-patented lossless data compression method known as DEFLATE, which is the same algorithm used in the zlib compression library.

The first version of the PNG specification was released on October 1, 1996 as a W3C Recommendation.

Various tools are available for optimizing .png files; they do this by :

  • removing ancillary chunks
  • reducing color depth
  • optimizing line-by-line filter choice
  • optimizing DEFLATE compression.

In general one must use a combination of 2 tools in sequence for optimal compression: one which optimizes filters and removes ancillary chunks, and one which optimizes DEFLATE.

Some common tools are :

  • OptiPNG
  • PNGOUT
  • Pngcrush
  • PngGauntlet

There are several online tools available on the web to optimize .png files :

  • PunyPNG, compression and image optimization tool developed by Gracepoint Berkeley
  • Smush.it, by Yahoo Developer Network

A list of some useful tutorials about image compression is shown below :

reCAPTCHA

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reCAPTCHA is a free CAPTCHA service provided by Google that helps to digitize books, newspapers and old time radio shows.

To archive human knowledge and to make information more accessible to the world, multiple projects are currently digitizing physical books that were written before the computer age. The book pages are being photographically scanned, and then transformed into text using “Optical Character Recognition” (OCR). One problem is that OCR is not perfect.

reCAPTCHA improves the process of digitizing books by sending words that cannot be read by computers to the Web in the form of CAPTCHAs for humans to decipher.
Each new word that cannot be read correctly by OCR is given to a user in conjunction with another word for which the answer is already known. The user is then asked to read both words. If they solve the one for which the answer is known, the system assumes their answer is correct for the new one. The system then gives the new image to a number of other people to determine, with higher confidence, whether the original answer was correct.

The technical backround of this technique has been published in a paper in the Science Magazine, Vol 321, 12 September 2008.

Polaroid Z340 Instant Digital Camera

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Polaroid Z340 Instant Digital Camera

Since the launch of the Polaroid Land Camera in 1948, the first instant camera, people around the world have enjoyed the magic of Polaroid instant photography. Today Polaroid announced the Z340 Instant Digital Camera,  a full-function 14.0 megapixel digital camera with an integrated ZINK® printer. The Polaroid Z340 camera delivers the same instant experience that is synonymous with the Polaroid brand: a simple, quick and easy way to capture, print, share and create with snapshots from our lives.

The Z340 camera produces vibrant photos with the patented ZINK Paper®, an advanced composite material with cyan, yellow, and magenta dye crystals embedded inside. Before printing, the embedded dye crystals are clear, so the ZINK Paper looks like regular white photo paper.

The Polaroid Z340 camera is available for $299.99 US$. The price for thirty sheets of Polaroid ZINK 3×4” Paper is 19.99 US$.

MotionPortrait

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MotionPortrait Inc is an japanese entertainment solution company that creates “Surprise and Impression” pursuing technology and creativity.

You can apply MotionPortrait as web sales promotion tools in various business scenes with its realistic expression, easy operation and low installation cost.  MotionPortrait provides its technology for various platforms such as mobile phones, the web and game consoles.

The most exciting applications for mobile phones (iOS, Android, …) are :

  • PhotoSpeak : 3D Talking Photo
  • 3D Animalizer : transforms you and your friends into 3D animals
  • uMovie : movies starring YOU
  • MillionFace : takes a single portrait photo and transforms it into over a million face variations in a 3D interactive movie

Lightbox scripts

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last update : April 11, 2012

Lightbox is a simple, unobtrusive script (modal dialog box) used to overlay images on the current webpage. It’s a snap to setup and works on all modern browsers. Lightbox was developed by Lokesh Dhakar.

The current version is 2.0.5 released on March 18th, 2011. It’s based on the Prototype Javascript  Framework and on script.aculo.us. There are specific modified versions available like ThickBox (developed by Cody Lindley, but no longer maintained), LyteBox (version 5.5, released on January 26, 2012 by Markus F. Hay) and others. Different plugin’s are available for WordPress.

A very minimal implementation of a lightbox (modal dialog box) is available at the Google code website. More scripts about modal dialog boxes are listed at the Designlabel website.

My favorite script is Lytebox developped by Markus F. Hay. Based on the Lightbox class that Lokesh Dhakar originally wrote, the purpose was to write a self-contained object that eliminated the dependency of the javascript frameworks prototype.js, effects.js, and scriptaculous.js. Lytebox supports iFrames.  Since the original version of Lytebox, major modifications were added as a result of user input to improve performance as well as slideshow support, themes support, HTML content support and many more configurable options that allow you to customize the look and feel of the software.

More informations about modal dialog boxes are available at the following links :

Standalone scripts :

jQuery based scripts :

Miscellanous :