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

Measurement and Modeling of Viewing-Condition-Dependent Spatio-Velocity Contrast Sensitivity Function.

Real Time Deformation Model of Liver Based on Structural Mechanics for Medical Training Systems and Its Accuracy Evaluation

In this paper, we present a novel process to control the skin melanin texture over a continuous range. The process is implemented on graphics hardware and can achieve real-time processing for a livevideo stream. A component map of melanin texture is utilized, which was extracted from a skin color image by our previous method [Tsumura et al. 2003]. A feature vector is calculated from the extracted melanin texture and shifted in a feature space to control the melanin texture over a continuous range. The feature space is constructed based on the texture database to extract physiologically plausible changes in the melanin texture.

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Real-time image-based control of skin melanin texture

In this research, the authors have developed a compact and fast measurement system for three-dimensional (3D) shape, normal vector, and bidirectional reflectance distribution function (BRDF) of a 3D object, e.g., a human face. Since the proposed system uses linear light sources and a luminous intensity distribution of these light sources, the BRDF can be measured for a short time and without large measurement space. The proposed system is evaluated by two methods whereby the authors confirmed its accuracy: by measuring sample known objects and by comparing actual and reproduced images.

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Development of Measurement System for Facial Physical Properties with Short-Distance Lighting

The resolution of a texture image in computer graphics is determined by the camera system used to take the image. Zooming in on the object beyond the optical resolution results in a loss of edges and details. We developed a “super resolution” method that uses a database of textures and texton substitution to create plausible high-frequency detail in enlarged texture images. Conventional example-based super-resolution techniques involve a high computational cost and users must provide the optimal parameters for the input image. Using texton in the wavelet space and a pixel-based (not a patch-based) substitution reduces the computational cost.

Yoichi Miyake

Papers

Measurement and Modeling of Viewing-Condition-Dependent Spatio-Velocity Contrast Sensitivity Function.

Real Time Deformation Model of Liver Based on Structural Mechanics for Medical Training Systems and Its Accuracy Evaluation

Real-time image-based control of skin melanin texture

Development of Measurement System for Facial Physical Properties with Short-Distance Lighting

Super Resolution Method Using Texton Substitution