Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

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Detecting faces in images: a survey

The measurement of meaning

to the Human Visual System (HVS).- Visual Attention.- Neurological Substrate of the HVS.- Visual Psychophysics.- Taxonomy and Models of Eye Movements.- Eye Tracking Systems.- Eye Tracking Techniques.- Head-Mounted System Hardware Installation.- Head-Mounted System Software Development.- Head-Mounted System Calibration.- Table-Mounted System Hardware Installation.- Table-Mounted System Software Development.- Table-Mounted System Calibration.- Eye Movement Analysis.- Eye Tracking Methodology.- Experimental Design.- Suggested Empirical Guidelines.- Case Studies.- Eye Tracking Applications.- Diversity and Types of Eye Tracking Applications.- Neuroscience and Psychology.- Industrial Engineering and Human Factors.- Marketing/Advertising.- Computer Science.- Conclusion.

Eye Tracking Methodology: Theory and Practice

Cell phones and other portable devices are equipped with a variety of technologies by which existing functionality can be improved, and new functionality can be provided. Some relate to visual search capabilities, and determining appropriate actions responsive to different image inputs. Others relate to processing of image data. Still others concern metadata generation, processing, and representation. Yet others relate to coping with fixed focus limitations of cell phone cameras, e.g., in reading digital watermark data. Still others concern user interface improvements. A great number of other features and arrangements are also detailed.

Methods and Systems for Content Processing

Transmittance spectra of fungi were estimated from seven-band optical micrographs. An optical microscope adjusted with a single-chip CCD camera with seven band filters was used for image acquisition. The Wiener method was applied to estimate the transmittance spectra of five species that belong to one genus of fungi. The Wiener estimation operator was calculated from transmittance spectra of 16 color transparencies and the corresponding camera responses. The estimated transmittance spectra were used for segmentation of conidia and hyphae in fungal optical micrographs; then the competitive learning in an artificial neural network was applied to the segmentation.

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Estimation of transmittance spectra from multiband micrographs of fungi and its application to segmentation of conidia and hyphae

In this article, we propose an effective method for gonio-spectral imaging of paper and cloth samples under oblique illumination conditions. High resolution gonio-spectral images are synthesized from a basic spectral image and gonio-monochrome images. The method is based on gonio-spectral image fusion composed of two components, conventional spatial fusion and geometrical fusion. The proposed geometrical fusion synthesizes images at different geometries and is introduced by modeling the optical reflection properties with a dichromatic reflection model. The validity of this method is confirmed by gonio-spectral measurements of paper samples, and experiments are performed on Japanese washi paper and European cloth.

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Gonio-spectral imaging of paper and cloth samples under oblique illumination conditions based on image fusion techniques

A multi-spectral imaging system is evaluated for mapping melanin density, total-hemoglobin density, and oxygen saturation in human skin. In this system, the distribution of pigments in human skin is estimated and displayed from digital video signals using three pre-computed “look-up” tables for color conversions. The accuracy of the system is analyzed based on computer simulation by changing the number of bands, quantization levels of the camera system, and dimensions of approximation for spectral reflectance. The optimal system is examined based on the results of computer simulation.

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Evaluating a Multi-Spectral Imaging System for Mapping Pigments in Human Skin

In this paper, a color difference is defined instead of the conventional color differences to evaluate gamut-mapping techniques for electronic endoscope images reproduced on CRT under environmental illuminant. This color difference is defined from Mahalanobis distance by using covariance matrices for differences of metric lightness, chroma and hue angle. The covariance matrices for endoscope images were obtained by psychophysical experiments. We compared the resultant matrices with those for natural scenes, and found the color difference was customized for the electronic endoscope images well.

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Color Gamut Mapping based on Mahalanobis Distance for Color Reproduction of Electronic Endoscope Image under Different Illuminant.

A video camera produces an image sequence with a specific frame rate (e.g. 30 frames per second). This imposes limits on the spatial resolution due to limited bandwidth. Image interpolation, such as bi-cubic interpolation, can increase the resolution, but yields a blurring of edges and image details. To create plausible high-frequency details in the blurred image, super-resolution technique has been a long studied area[Baker and Kanade 2002; Capel and Zisserman 2003]. However, it is difficult to apply these methods to a video sequence, since [Capel and Zisserman 2003] requires multiple images, and [Baker and Kanade 2002] requires a high computational cost. In order to resolve these problems, we proposed a method called "texton substitution"[Kamimura et al. 2006]. In this paper, we improve the method of texton substitution by using temporal connection.

Yoichi Miyake

Papers

Estimation of transmittance spectra from multiband micrographs of fungi and its application to segmentation of conidia and hyphae

Gonio-spectral imaging of paper and cloth samples under oblique illumination conditions based on image fusion techniques

Evaluating a Multi-Spectral Imaging System for Mapping Pigments in Human Skin

Color Gamut Mapping based on Mahalanobis Distance for Color Reproduction of Electronic Endoscope Image under Different Illuminant.

Video super-resolution using texton substitution