Statistical pattern modeling in vision-based quality control systems

Machine vision technology improves productivity and quality management and provides a competitive advantage to industries that employ this technology. In this article, visual inspection and quality control theory are combined to develop a robust inspection system with manufacturing applications. The inspection process might be defined as the one used to determine if a given product fulfills a priori specifications, which are the quality standard. In the case of visual inspection, these specifications include the absence of defects, such as lack (or excess) of material, homogeneous visual aspect, required color, predetermined texture, etc. The characterization of the visual aspect of metallic surfaces is studied using quality control chars, which are a graphical technique used to compare on-line capabilities of a product with respect to these specifications. Original algorithms are proposed for implementation in automated visual inspection applications with on-line execution requirements. The proposed artificial vision method is a hybrid between the two usual methods of pattern comparison and theoretical decision. It incorporates quality control theory to statistically model the pattern for defect-free products. Specifically, individual control charts with 6-sigma limits are set so the inspection error is minimized. Experimental studies with metallic surfaces help demonstrate the efficacy and robustness of the proposed methodology.Publicad

Statistical spatial color information modeling in images and applications

Image processing, among its vast applications, has proven particular efficiency in quality control systems. Quality control systems such as the ones in the food industry, fruits and meat industries, pharmaceutic, and hardness testing are highly dependent on the accuracy of the algorithms used to extract image feature vectors and process them. Thus, the need to build better quality systems is tied to the progress in the field of image processing. Color histograms have been widely and successfully used in many computer vision and image processing applications. However, they do not include any spatial information. We propose statistical models to integrate both color and spatial information. Our first model is based on finite mixture models which have been applied to different computer vision, image processing and pattern recognition tasks. The majority of the work done concerning finite mixture models has focused on mixtures for continuous data. However, many applications involve and generate discrete data for which discrete mixtures are better suited. In this thesis, we investigate the problem of discrete data modeling using finite mixture models. We propose a novel, well motivated mixture that we call a multinomial generalized Dirichlet mixture. Our second model is based on finite multiple-Bernoulli mixtures. For the estimation of the model's parameters, we use a maximum a posteriori (MAP) approach through deterministic annealing expectation maximization (DAEM). Smoothing priors to the components parameters are introduced to stabilize the estimation. The selection of the number of clusters is based on stochastic complexit

Negative Results in Computer Vision: A Perspective

A negative result is when the outcome of an experiment or a model is not what is expected or when a hypothesis does not hold. Despite being often overlooked in the scientific community, negative results are results and they carry value. While this topic has been extensively discussed in other fields such as social sciences and biosciences, less attention has been paid to it in the computer vision community. The unique characteristics of computer vision, particularly its experimental aspect, call for a special treatment of this matter. In this paper, I will address what makes negative results important, how they should be disseminated and incentivized, and what lessons can be learned from cognitive vision research in this regard. Further, I will discuss issues such as computer vision and human vision interaction, experimental design and statistical hypothesis testing, explanatory versus predictive modeling, performance evaluation, model comparison, as well as computer vision research culture

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Deep Markov Random Field for Image Modeling

Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact that conventional MRFs formulations tend to use simplistic factors to capture local patterns. In this paper, we move beyond such limitations, and propose a novel MRF model that uses fully-connected neurons to express the complex interactions among pixels. Through theoretical analysis, we reveal an inherent connection between this model and recurrent neural networks, and thereon derive an approximated feed-forward network that couples multiple RNNs along opposite directions. This formulation combines the expressive power of deep neural networks and the cyclic dependency structure of MRF in a unified model, bringing the modeling capability to a new level. The feed-forward approximation also allows it to be efficiently learned from data. Experimental results on a variety of low-level vision tasks show notable improvement over state-of-the-arts.Comment: Accepted at ECCV 201

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version