Higher-order image statistics for unsupervised, information-theoretic, adaptive, image filtering

technical reportThe restoration of images is an important and widely studied problem in computer vision and image processing. Various image filtering strategies have been effective, but invariably make strong assumptions about the properties of the signal and/or degradation. Therefore, these methods typically lack the generality to be easily applied to new applications or diverse image collections. This paper describes a novel unsupervised, informationtheoretic, adaptive filter (UINTA) that improves the predictability of pixel intensities from their neighborhoods by decreasing the joint entropy between them. Thus UINTA automatically discovers the statistical properties of the signal and can thereby restore a wide spectrum of images and applications. This paper describes the formulation required to minimize the joint entropy measure, presents several important practical considerations in estimating image-region statistics, and then presents results on both real and synthetic data

Color Separation for Image Segmentation

Image segmentation is a fundamental problem in computer vision that has drawn intensive research attention during the past few decades, resulting in a variety of segmentation algorithms. Segmentation is often formulated as a Markov random field (MRF) and the solution corresponding to the maximum a posteriori probability (MAP) is found using energy minimiza- tion framework. Many standard segmentation techniques rely on foreground and background appearance models given a priori. In this case the corresponding energy can be efficiently op- timized globally. If the appearance models are not known, the energy becomes NP-hard, and many methods resort to iterative schemes that jointly optimize appearance and segmentation. Such algorithms can only guarantee local minimum. Here we propose a new energy term explicitly measuring L1 distance between the object and background appearance models that can be globally maximized in one graph cut. Our method directly tries to minimize the appearance overlap between the segments. We show that in many applications including interactive segmentation, shape matching, segmentation from stereo pairs and saliency segmentation our simple term makes NP-hard segmentation functionals unnecessary and renders good segmentation performance both qualitatively and quantitatively

Brain tumor segmentation with minimal user assistance

In this thesis, we propose a brain tumor segmentation system that requires only 4 clicks from users to specify a tight bounding box that completely contains the tumor. We convert the segmentation problem to an energy minimization problem. We utilize the basic energy function that combines intensity appearance and boundary smoothness. Global and local appearance models are experimented and compared in our work. The basic energy function does not assume any shape prior and thus leads to unrealistic shapes. We take the advantage of the fact that most of the tumors are approximately convex in shape and incorporate the star shape prior to prohibit unlikely segmentations. Another problem with the basic energy function is the undersegmentation problem. With the bounding box provided by the user, we are able to have a rough idea of the tumor size. Therefore, to encourage the segmentation to be a certain size, we add volumetric bias to our energy, which helps solve this problem. We also try to model the tumor as multi-region object where regions have distinct appearance. Specifically, we incorporate interior+exterior model for the tumor into our energy function. Our final result is promising in terms of f-measure. Our best performance for 88 volumes is 87% using volumetric ballooning

Bayesian Modeling of Dynamic Scenes for Object Detection

Abstract—Accurate detection of moving objects is an important precursor to stable tracking or recognition. In this paper, we present an object detection scheme that has three innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic backgrounds. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, multimodal spatial uncertainties and complex dependencies between the domain (location) and range (color) are directly modeled. We propose a model of the background as a single probability density. Second, temporal persistence is proposed as a detection criterion. Unlike previous approaches to object detection which detect objects by building adaptive models of the background, the foreground is modeled to augment the detection of objects (without explicit tracking) since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the proposed method is performed and presented on a diverse set of dynamic scenes. Index Terms—Object detection, kernel density estimation, joint domain range, MAP-MRF estimation. æ

Algorithms for Hyper-Parameter Optimization

International audienceSeveral recent advances to the state of the art in image classification benchmarks have come from better configurations of existing techniques rather than novel ap- proaches to feature learning. Traditionally, hyper-parameter optimization has been the job of humans because they can be very efficient in regimes where only a few trials are possible. Presently, computer clusters and GPU processors make it pos- sible to run more trials and we show that algorithmic approaches can find better results. We present hyper-parameter optimization results on tasks of training neu- ral networks and deep belief networks (DBNs). We optimize hyper-parameters using random search and two new greedy sequential methods based on the ex- pected improvement criterion. Random search has been shown to be sufficiently efficient for learning neural networks for several datasets, but we show it is unreli- able for training DBNs. The sequential algorithms are applied to the most difficult DBN learning problems from [1] and find significantly better results than the best previously reported. This work contributes novel techniques for making response surface models P(y|x) in which many elements of hyper-parameter assignment (x) are known to be irrelevant given particular values of other elements