Leveraging Colour Segmentation for Upper-Body Detection

This paper presents an upper-body detection algorithm that extends classical shape-based detectors through the use of additional semantic colour segmentation cues. More precisely, candidate upper-body image patches produced by a base detector are soft-segmented using a multi-class probabilistic colour segmentation algorithm that leverages spatial as well as colour prior distributions for different semantic object regions (skin, hair, clothing, background). These multi-class soft segmentation maps are then classified as true or false upper-bodies. By further fusing the score of this latter classifier with the base detection score, the method shows a performance improvement on three different public datasets and using two different upper-body base detectors, demonstrating the complementarity of the contextual semantic colour segmentation and the base detector

Identifying person re-occurrences for personal photo management applications

Automatic identification of "who" is present in individual digital images within a photo management system using only content-based analysis is an extremely difficult problem. The authors present a system which enables identification of person reoccurrences within a personal photo management application by combining image content-based analysis tools with context data from image capture. This combined system employs automatic face detection and body-patch matching techniques, which collectively facilitate identifying person re-occurrences within images grouped into events based on context data. The authors introduce a face detection approach combining a histogram-based skin detection model and a modified BDF face detection method to detect multiple frontal faces in colour images. Corresponding body patches are then automatically segmented relative to the size, location and orientation of the detected faces in the image. The authors investigate the suitability of using different colour descriptors, including MPEG-7 colour descriptors, color coherent vectors (CCV) and color correlograms for effective body-patch matching. The system has been successfully integrated into the MediAssist platform, a prototype Web-based system for personal photo management, and runs on over 13000 personal photos

Accelerated hardware video object segmentation: From foreground detection to connected components labelling

This is the preprint version of the Article - Copyright @ 2010 ElsevierThis paper demonstrates the use of a single-chip FPGA for the segmentation of moving objects in a video sequence. The system maintains highly accurate background models, and integrates the detection of foreground pixels with the labelling of objects using a connected components algorithm. The background models are based on 24-bit RGB values and 8-bit gray scale intensity values. A multimodal background differencing algorithm is presented, using a single FPGA chip and four blocks of RAM. The real-time connected component labelling algorithm, also designed for FPGA implementation, run-length encodes the output of the background subtraction, and performs connected component analysis on this representation. The run-length encoding, together with other parts of the algorithm, is performed in parallel; sequential operations are minimized as the number of run-lengths are typically less than the number of pixels. The two algorithms are pipelined together for maximum efficiency

Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation

Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models. Mapping from the 2D image space to the prediction space is difficult: perspective ambiguities make the loss function noisy and training data is scarce. In this paper, we propose a novel approach (Neural Body Fitting (NBF)). It integrates a statistical body model within a CNN, leveraging reliable bottom-up semantic body part segmentation and robust top-down body model constraints. NBF is fully differentiable and can be trained using 2D and 3D annotations. In detailed experiments, we analyze how the components of our model affect performance, especially the use of part segmentations as an explicit intermediate representation, and present a robust, efficiently trainable framework for 3D human pose estimation from 2D images with competitive results on standard benchmarks. Code will be made available at http://github.com/mohomran/neural_body_fittingComment: 3DV 201

Utilising the Intel RealSense camera for measuring health outcomes in clinical research

Applications utilising 3D Camera technologies for the measurement of health outcomes in the health and wellness sector continues to expand. The Intel® RealSense™ is one of the leading 3D depth sensing cameras currently available on the market and aligns itself for use in many applications, including robotics, automation, and medical systems. One of the most prominent areas is the production of interactive solutions for rehabilitation which includes gait analysis and facial tracking. Advancements in depth camera technology has resulted in a noticeable increase in the integration of these technologies into portable platforms, suggesting significant future potential for pervasive in-clinic and field based health assessment solutions. This paper reviews the Intel RealSense technology’s technical capabilities and discusses its application to clinical research and includes examples where the Intel RealSense camera range has been used for the measurement of health outcomes. This review supports the use of the technology to develop robust, objective movement and mobility-based endpoints to enable accurate tracking of the effects of treatment interventions in clinical trials

Leveraging Unstructured Image Data for Product Quality Improvement

Recently, traditional quality assurance methods, which often require human expertise, have been accompanied by more automated methods that use machine learning technology. These methods offer manufacturers to reduce error rates and, consequently, to increase margins as well. In particular, predictive quality assurance (Pre QA) allows to minimize expenses by feeding back information from product returns and quality checks into the early product development. However, Pre QA requires detailed information about previous quality problems which is not always readily available in a structured form. In this paper, we therefore discuss the potential of leveraging initially unstructured information in the form of images, taken either during quality checks or by customers when returning a product, to the end of product quality improvement. We furthermore show how this might be realized in practice using the case of fashion manufacturing as an example