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

PROBLEM TO BE SOLVED: To provide the illuminating light by which an effect similar to color emphasizing processing suitable for an object for observation can be obtained and a delicate color change in the observing can be clearly observed SOLUTION: A light source device 2 has a plurality of a light source A11, a light source B12 and a light source C13 composed of white light sources, and a filter A15, a filter B16 and a filter C17 to respectively pass a prescribed wave length area are arranged on emitting optical paths of the respective light sources 11 to 13 The light from the respective light sources 11 to 13 becomes the light having respectively different spectral characteristics through the filters 15 to 17, and the synthetic light by synthesizing these light is made incident on the incident end of a light guide 7 of a connected endoscope 1 as the illuminating light This illuminating light has whiteness similar to that of a xenon lamp, and becomes the light having synthetic spectral characteristics such as a color difference in an observing image of the object becomes large

Endoscope light source device

A spectral color reproduction method is proposed based on a vector error diffusion method. A low-dimensional linear model is used to approximate reflectance spectra, and the vector error diffusion is performed in a score vector space of the linear model. A suitable set of primary colors is also selected from oil colors. Presented method performed better than conventional colorimetric vector error diffusion method for robust color reproduction for a change of illuminant.

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Vector Error Diffusion Method for Spectral Color Reproduction.

This paper proposes a new and simple algorithm for face extraction from a color image. The approach detects the lip region in a face object. First, the lip- and skin-color pixels in an image are extracted on the basis of statistical probability analysis. These lip- and skin-color pixels are segmented separately by using binary image processing techniques to produce lip- and skin-color regions. Each region that has a skin-color region and one or more lip-color regions as its subset regions is nominated as a face candidate. To detect only the face objects from the face candidates, the algorithm evaluates the values of seven pattern variables of those face candidates. Aface candidate with all its seven pattern variable values within the valid range values of face object class is detected as a face object. Verifications of the proposed algorithm were provided by the experimental results that gave 91.8% detection of face objects from 104 sample images. This is significant for locating or detecting the faces in color images.

Algorithm for face extraction based on lip detection

A multiband imaging system with multi-angle illumination and a processing algorithm for multiband images acquired by the system are presented for extracting goniospectral information of 3D objects. Based on a dichromatic reflection model, multiband images are separated into diffuse and specular components. Then, the model parameters are determined pixelwise and archived. Using these parameters, an image of the 3D object under an arbitrary spectral and spatial distribution of illuminant can be predicted and displayed. A fundamental experiment to confirm the principle of this method is presented.

Yoichi Miyake

Papers

Endoscope light source device

Vector Error Diffusion Method for Spectral Color Reproduction.

Algorithm for face extraction based on lip detection

Goniospectral Imaging of 3D Objects.

Picture Processing by Computer:Boundary-line Extraction