Image registration and visualization of in situ gene expression images.

In the age of high-throughput molecular biology techniques, scientists have incorporated the methodology of in-situ hybridization to map spatial patterns of gene expression. In order to compare expression patterns within a common tissue structure, these images need to be registered or organized into a common coordinate system for alignment to a reference or atlas images. We use three different image registration methodologies (manual; correlation based; mutual information based) to determine the common coordinate system for the reference and in-situ hybridization images. All three methodologies are incorporated into a Matlab tool to visualize the results in a user friendly way and save them for future work. Our results suggest that the user-defined landmark method is best when considering images from different modalities; automated landmark detection is best when the images are expected to have a high degree of consistency; and the mutual information methodology is useful when the images are from the same modality

The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work).

This paper explores the theoretical consequences of a simple assumption: the computational goal of the feedforward path in the ventral stream -- from V1, V2, V4 and to IT -- is to discount image transformations, after learning them during development

Generalized least squares-based parametric motion estimation and segmentation

El análisis del movimiento es uno de los campos más importantes de la visión por computador. Esto es debido a que el mundo real está en continuo movimiento y es obvio que podremos obtener mucha más información de escenas en movimiento que de escenas estáticas. En esta tesis se ha trabajado principalmente en desarrollar algoritmos de estimación de movimiento para su aplicación a problemas de registrado de imágenes y a problemas de segmentación del movimiento. Uno de los principales objetivos de este trabajo es desarrollar una técnica de registrado de imágenes de gran exactitud, tolerante a outliers y que sea capaz de realizar su labor incluso en la presencia de deformaciones de gran magnitud tales como traslaciones, rotaciones, cambios de escala, cambios de iluminación globales y no espacialmente uniformes, etc. Otro de los objetivos de esta tesis es trabajar en problemas de estimación y la segmentación del movimiento en secuencias de dos imágenes de forma casi simultánea y sin conocimiento a priori del número de modelos de movimiento presentes. Los experimentos mostrados en este trabajo demuestran que los algoritmos propuestos en esta tesis obtienen resultados de gran exactitud.This thesis proposes several techniques related with the motion estimation problem. In particular, it deals with global motion estimation for image registration and motion segmentation. In the first case, we will suppose that the majority of the pixels of the image follow the same motion model, although the possibility of a large number of outliers are also considered. In the motion segmentation problem, the presence of more than one motion model will be considered. In both cases, sequences of two consecutive grey level images will be used. A new generalized least squares-based motion estimator will be proposed. The proposed formulation of the motion estimation problem provides an additional constraint that helps to match the pixels using image gradient information. That is achieved thanks to the use of a weight for each observation, providing high weight values to the observations considered as inliers, and low values to the ones considered as outliers. To avoid falling in a local minimum, the proposed motion estimator uses a Feature-based method (SIFT-based) to obtain good initial motion parameters. Therefore, it can deal with large motions like translation, rotations, scales changes, viewpoint changes, etc. The accuracy of our approach has been tested using challenging real images using both affine and projective motion models. Two Motion Estimator techniques, which use M-Estimators to deal with outliers into a iteratively reweighted least squared-based strategy, have been selected to compare the accuracy of our approach. The results obtained have showed that the proposed motion estimator can obtain as accurate results as M-Estimator-based techniques and even better in most cases. The problem of estimating accurately the motion under non-uniform illumination changes will also be considered. A modification of the proposed global motion estimator will be proposed to deal with this kind of illumination changes. In particular, a dynamic image model where the illumination factors are functions of the localization will be used replacing the brightens constancy assumption allowing for a more general and accurate image model. Experiments using challenging images will be performed showing that the combination of both techniques is feasible and provides accurate estimates of the motion parameters even in the presence of strong illumination changes between the images. The last part of the thesis deals with the motion estimation and segmentation problem. The proposed algorithm uses temporal information, by using the proposed generalized least-squares motion estimation process and spatial information by using an iterative region growing algorithm which classifies regions of pixels into the different motion models present in the sequence. In addition, it can extract the different moving regions of the scene while estimating its motion quasi-simultaneously and without a priori information of the number of moving objects in the scene. The performance of the algorithm will be tested on synthetic and real images with multiple objects undergoing different types of motion

Generalized least squares-based parametric motion estimation and segmentation

El análisis del movimiento es uno de los campos más importantes de la visión por computador. Esto es debido a que el mundo real está en continuo movimiento y es obvio que podremos obtener mucha más información de escenas en movimiento que de escenas estáticas. En esta tesis se ha trabajado principalmente en desarrollar algoritmos de estimación de movimiento para su aplicación a problemas de registrado de imágenes y a problemas de segmentación del movimiento. Uno de los principales objetivos de este trabajo es desarrollar una técnica de registrado de imágenes de gran exactitud, tolerante a outliers y que sea capaz de realizar su labor incluso en la presencia de deformaciones de gran magnitud tales como traslaciones, rotaciones, cambios de escala, cambios de iluminación globales y no espacialmente uniformes, etc. Otro de los objetivos de esta tesis es trabajar en problemas de estimación y la segmentación del movimiento en secuencias de dos imágenes de forma casi simultánea y sin conocimiento a priori del número de modelos de movimiento presentes. Los experimentos mostrados en este trabajo demuestran que los algoritmos propuestos en esta tesis obtienen resultados de gran exactitud.This thesis proposes several techniques related with the motion estimation problem. In particular, it deals with global motion estimation for image registration and motion segmentation. In the first case, we will suppose that the majority of the pixels of the image follow the same motion model, although the possibility of a large number of outliers are also considered. In the motion segmentation problem, the presence of more than one motion model will be considered. In both cases, sequences of two consecutive grey level images will be used. A new generalized least squares-based motion estimator will be proposed. The proposed formulation of the motion estimation problem provides an additional constraint that helps to match the pixels using image gradient information. That is achieved thanks to the use of a weight for each observation, providing high weight values to the observations considered as inliers, and low values to the ones considered as outliers. To avoid falling in a local minimum, the proposed motion estimator uses a Feature-based method (SIFT-based) to obtain good initial motion parameters. Therefore, it can deal with large motions like translation, rotations, scales changes, viewpoint changes, etc. The accuracy of our approach has been tested using challenging real images using both affine and projective motion models. Two Motion Estimator techniques, which use M-Estimators to deal with outliers into a iteratively reweighted least squared-based strategy, have been selected to compare the accuracy of our approach. The results obtained have showed that the proposed motion estimator can obtain as accurate results as M-Estimator-based techniques and even better in most cases. The problem of estimating accurately the motion under non-uniform illumination changes will also be considered. A modification of the proposed global motion estimator will be proposed to deal with this kind of illumination changes. In particular, a dynamic image model where the illumination factors are functions of the localization will be used replacing the brightens constancy assumption allowing for a more general and accurate image model. Experiments using challenging images will be performed showing that the combination of both techniques is feasible and provides accurate estimates of the motion parameters even in the presence of strong illumination changes between the images. The last part of the thesis deals with the motion estimation and segmentation problem. The proposed algorithm uses temporal information, by using the proposed generalized least-squares motion estimation process and spatial information by using an iterative region growing algorithm which classifies regions of pixels into the different motion models present in the sequence. In addition, it can extract the different moving regions of the scene while estimating its motion quasi-simultaneously and without a priori information of the number of moving objects in the scene. The performance of the algorithm will be tested on synthetic and real images with multiple objects undergoing different types of motion

Advances in Manipulation and Recognition of Digital Ink

Handwriting is one of the most natural ways for a human to record knowledge. Recently, this type of human-computer interaction has received increasing attention due to the rapid evolution of touch-based hardware and software. While hardware support for digital ink reached its maturity, algorithms for recognition of handwriting in certain domains, including mathematics, are lacking robustness. Simultaneously, users may possess several pen-based devices and sharing of training data in adaptive recognition setting can be challenging. In addition, resolution of pen-based devices keeps improving making the ink cumbersome to process and store. This thesis develops several advances for efficient processing, storage and recognition of handwriting, which are applicable to the classification methods based on functional approximation. In particular, we propose improvements to classification of isolated characters and groups of rotated characters, as well as symbols of substantially different size. We then develop an algorithm for adaptive classification of handwritten mathematical characters of a user. The adaptive algorithm can be especially useful in the cloud-based recognition framework, which is described further in the thesis. We investigate whether the training data available in the cloud can be useful to a new writer during the training phase by extracting styles of individuals with similar handwriting and recommending styles to the writer. We also perform factorial analysis of the algorithm for recognition of n-grams of rotated characters. Finally, we show a fast method for compression of linear pieces of handwritten strokes and compare it with an enhanced version of the algorithm based on functional approximation of strokes. Experimental results demonstrate validity of the theoretical contributions, which form a solid foundation for the next generation handwriting recognition systems

Automated framework for robust content-based verification of print-scan degraded text documents

Fraudulent documents frequently cause severe financial damages and impose security breaches to civil and government organizations. The rapid advances in technology and the widespread availability of personal computers has not reduced the use of printed documents. While digital documents can be verified by many robust and secure methods such as digital signatures and digital watermarks, verification of printed documents still relies on manual inspection of embedded physical security mechanisms.The objective of this thesis is to propose an efficient automated framework for robust content-based verification of printed documents. The principal issue is to achieve robustness with respect to the degradations and increased levels of noise that occur from multiple cycles of printing and scanning. It is shown that classic OCR systems fail under such conditions, moreover OCR systems typically rely heavily on the use of high level linguistic structures to improve recognition rates. However inferring knowledge about the contents of the document image from a-priori statistics is contrary to the nature of document verification. Instead a system is proposed that utilizes specific knowledge of the document to perform highly accurate content verification based on a Print-Scan degradation model and character shape recognition. Such specific knowledge of the document is a reasonable choice for the verification domain since the document contents are already known in order to verify them.The system analyses digital multi font PDF documents to generate a descriptive summary of the document, referred to as \Document Description Map" (DDM). The DDM is later used for verifying the content of printed and scanned copies of the original documents. The system utilizes 2-D Discrete Cosine Transform based features and an adaptive hierarchical classifier trained with synthetic data generated by a Print-Scan degradation model. The system is tested with varying degrees of Print-Scan Channel corruption on a variety of documents with corruption produced by repetitive printing and scanning of the test documents. Results show the approach achieves excellent accuracy and robustness despite the high level of noise

Handwritten Character Recognition Using Elastic Matching Based On Class-Dependent Deformation Model

For handwritten character recognition, a new elastic image matching (EM) technique based on a class-dependent deformation model is proposed. In the deformation model, any deformation of a class is described by a linear combination of eigen-deformations, which are intrinsic deformation directions of the class. The eigen-deformations can be estimated statistically from the actual deformations of handwritten characters. Experimental results show that the proposed technique can attain higher recognition rates than conventional EM techniques based on class-independent deformation models. The results also show the superiority of the proposed technique over those conventional EM techniques in computational efficiency

Learning from one example in machine vision by sharing probability densities

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 125-130).Human beings exhibit rapid learning when presented with a small number of images of a new object. A person can identify an object under a wide variety of visual conditions after having seen only a single example of that object. This ability can be partly explained by the application of previously learned statistical knowledge to a new setting. This thesis presents an approach to acquiring knowledge in one setting and using it in another. Specifically, we develop probability densities over common image changes. Given a single image of a new object and a model of change learned from a different object, we form a model of the new object that can be used for synthesis, classification, and other visual tasks. We start by modeling spatial changes. We develop a framework for learning statistical knowledge of spatial transformations in one task and using that knowledge in a new task. By sharing a probability density over spatial transformations learned from a sample of handwritten letters, we develop a handwritten digit classifier that achieves 88.6% accuracy using only a single hand-picked training example from each class. The classification scheme includes a new algorithm, congealing, for the joint alignment of a set of images using an entropy minimization criterion. We investigate properties of this algorithm and compare it to other methods of addressing spatial variability in images. We illustrate its application to binary images, gray-scale images, and a set of 3-D neonatal magnetic resonance brain volumes.Next, we extend the method of change modeling from spatial transformations to color transformations. By measuring statistically common joint color changes of a scene in an office environment, and then applying standard statistical techniques such as principal components analysis, we develop a probabilistic model of color change. We show that these color changes, which we call color flows, can be shared effectively between certain types of scenes. That is, a probability density over color change developed by observing one scene can provide useful information about the variability of another scene. We demonstrate a variety of applications including image synthesis, image matching, and shadow detection.by Erik G. Miller.Ph.D

Fast and robust image feature matching methods for computer vision applications

Service robotic systems are designed to solve tasks such as recognizing and manipulating objects, understanding natural scenes, navigating in dynamic and populated environments. It's immediately evident that such tasks cannot be modeled in all necessary details as easy as it is with industrial robot tasks; therefore, service robotic system has to have the ability to sense and interact with the surrounding physical environment through a multitude of sensors and actuators. Environment sensing is one of the core problems that limit the deployment of mobile service robots since existing sensing systems are either too slow or too expensive. Visual sensing is the most promising way to provide a cost effective solution to the mobile robot sensing problem. It's usually achieved using one or several digital cameras placed on the robot or distributed in its environment. Digital cameras are information rich sensors and are relatively inexpensive and can be used to solve a number of key problems for robotics and other autonomous intelligent systems, such as visual servoing, robot navigation, object recognition, pose estimation, and much more. The key challenges to taking advantage of this powerful and inexpensive sensor is to come up with algorithms that can reliably and quickly extract and match the useful visual information necessary to automatically interpret the environment in real-time. Although considerable research has been conducted in recent years on the development of algorithms for computer and robot vision problems, there are still open research challenges in the context of the reliability, accuracy and processing time. Scale Invariant Feature Transform (SIFT) is one of the most widely used methods that has recently attracted much attention in the computer vision community due to the fact that SIFT features are highly distinctive, and invariant to scale, rotation and illumination changes. In addition, SIFT features are relatively easy to extract and to match against a large database of local features. Generally, there are two main drawbacks of SIFT algorithm, the first drawback is that the computational complexity of the algorithm increases rapidly with the number of key-points, especially at the matching step due to the high dimensionality of the SIFT feature descriptor. The other one is that the SIFT features are not robust to large viewpoint changes. These drawbacks limit the reasonable use of SIFT algorithm for robot vision applications since they require often real-time performance and dealing with large viewpoint changes. This dissertation proposes three new approaches to address the constraints faced when using SIFT features for robot vision applications, Speeded up SIFT feature matching, robust SIFT feature matching and the inclusion of the closed loop control structure into object recognition and pose estimation systems. The proposed methods are implemented and tested on the FRIEND II/III service robotic system. The achieved results are valuable to adapt SIFT algorithm to the robot vision applications