THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

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

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

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VAN DIJK, TEUN A., News as Discourse Hillsdale, N.J.: Lawrence Erlbaum, 1988. $29.95 cloth. This book attempts the development of a "new, interdisciplinary theory of news in the press" (p. vii). It represents a very ambitious and somewhat speculative effort to weave together a broad range of existing news research approaches into a coherent, heuristic framework. Van Dijk succeeds in providing a useful summary of the literature in news research. Especially valuable is his discussion of recent European research. However, the overall framework is still at an early stage of development. Its utility remains to be demonstrated by future research. For some time now, communication researchers have talked of passing paradigms and ferment in the field. With the decline of past paradigms, our discipline has been left with a hodgepodge of small-scale theories. This is particularly true in the area of news research where various narrative, discourse and information processing theories abound. Van Dijk's book may signal a new era, an era in which efforts will be made to integrate existing conceptual fragments into broader frameworks. His work may be seen as providing a model for others who seek to make sense of our current proliferation of theories. Van Dijk's approach is centered in the tradition of discourse analysis, which evolved out of an integration of literary analysis and linguistics. However, he has aggressively modified earlier forms of discourse analysis in an effort to incorporate insights into the structure and interpretation of discourse derived from cognitive psychology. He is not content to simply apply discourse analysis to the evaluation of news stories. He recognizes the utility of constructing an approach which also considers the production of news by media practitioners and the interpretation of news by audience members. It is these broader concerns which set van Dijk's approach apart from previous analyses of news content. A central concept in van Dijk's theory is the notion of story schemas, which are defined as implicit structures that underlie typical stories. These schemas permit the easy production of news and also facilitate its interpretation by news consumers. The schema concept is at once powerful and ambiguous. There is growing research evidence that demonstrates the utility of positing the existence of cognitive structures (schemas) in people's minds which are activated by content cues and guide interpretion of all forms of communication. The schema concept helps to explain why complex and seemingly ambiguous messages often are easily interpreted by audience members. It also can explain why the same message can be interpreted in highly discrepent ways. If messages contain conflicting cues that lead people to activate different schemas, or if people don't share a homogeneous set of schemas, then it is likely that many contrasting interpretations of story content will be developed. But despite growing consensus concerning the utility of schema as a concept, researchers remain quite divided over both its definition and the type of research that will lead to the most useful findings. …

News as Discourse

In this paper, we consider the problem of estimating the spatiotemporal alignment between N unsynchronized video sequences of the same dynamic 3D scene, captured from distinct viewpoints. Unlike most existing methods, which work for N = 2 and rely on a computationally intensive search in the space of temporal alignments, we present a novel approach that reduces the problem for general N to the robust estimation of a single line in RN. This line captures all temporal relations between the sequences and can be computed without any prior knowledge of these relations. Considering that the spatial alignment is captured by the parameters of fundamental matrices, an iterative algorithm is used to refine simultaneously the parameters representing the temporal and spatial relations between the sequences. Experimental results with real-world and synthetic sequences show that our method can accurately align the videos even when they have large misalignments (e.g., hundreds of frames), when the problem is seemingly ambiguous (e.g., scenes with roughly periodic motion), and when accurate manual alignment is difficult (e.g., due to slow-moving objects).

Linear Sequence-to-Sequence Alignment

In this paper we study the use of computer vision techniques for for underwater visual tracking and counting of fishes in vivo. The methodology is based on the application of a Bayesian filtering technique that enables tracking of objects whose number may vary over time. Unlike existing fish-counting methods, this approach provides adequate means for the acquisition of relevant information about characteristics of different fish species such as swimming ability, time of migration and peak flow rates. The system is also able to estimate fish trajectories over time, which can be further used to study their behaviors when swimming in regions of interest. Our experiments demonstrate that the proposed method can operate reliably under severe environmental changes (e.g. variations in water turbidity) and handle problems such as occlusions or large inter-frame motions. The proposed approach was successfully validated with real-world video streams, achieving overall accuracy as high as 81%.

/pdf/particle-filter-based-predictive-tracking-for-robust-fish-8a77g0yxuh.pdf

Particle Filter-Based Predictive Tracking for Robust Fish Counting

We present a novel approach for temporally aligning N unsynchronized sequences of a dynamic 3D scene, captured from distinct viewpoints. Unlike existing methods, which work for N = 2 and rely on a computationally-intensive search in the space of temporal alignments, we reduce the problem for general N to the robust estimation of a single line in RN. This line captures all temporal relations between the sequences and can be computed without any prior knowledge of these relations. Experimental results show that our method can accurately align sequences even when they have large mis-alignments (e.g., hundreds of frames), when the problem is seemingly ambiguous (e.g., scenes with roughly periodic motion), and when accurate manual alignment is difficult (e.g., due to slow-moving objects).

Linear sequence-to-sequence alignment

This letter presents a new local feature descriptor for problems related to multispectral images. Most previous approaches are typically based on descriptors designed to work with images uniquely captured in the visible light spectrum. In contrast, this letter proposes a descriptor termed a multispectral feature descriptor (MFD) that is especially developed, such that it can be employed with image data acquired at different frequencies across the electromagnetic spectrum. The performance of the MFD is evaluated by using three data sets composed of images obtained in visible light and infrared spectra, and its performance is compared with those of state-of-the-art algorithms, such as edge-oriented histogram (EOH) and log-Gabor histogram descriptor (LGHD). The experimental results indicate that the computational efficiency of MFD exceeds those of EOH and LGHD, and that the precision and recall values of MFD are statistically comparable to the corresponding values of the forementioned algorithms.

A Local Feature Descriptor Based on Log-Gabor Filters for Keypoint Matching in Multispectral Images

We present a novel fish classification methodology based on a robust feature selection technique. Unlike existing works for fish classification, which propose descriptors and do not analyze their individual impacts in the whole classification task, we propose a general set of features and their correspondent weights that should be used as a priori information by the classifier. In this sense, instead of studying techniques for improving the classifiers structure itself, we consider it as a "black box" and focus our research in the determination of which input information must bring a robust fish discrimination. All the experiments were performed with fish species of Rio Grande river in Minas Gerais, Brazil. This work has been developed as part of a wider research [3], which has as main goal the development of effective fish ladders for the Brazilian dams.

/pdf/determining-the-appropriate-feature-set-for-fish-3cmoimk3j6.pdf

Flávio Luis Cardeal Pádua

Papers

Linear Sequence-to-Sequence Alignment

Particle Filter-Based Predictive Tracking for Robust Fish Counting

Linear sequence-to-sequence alignment

A Local Feature Descriptor Based on Log-Gabor Filters for Keypoint Matching in Multispectral Images

Determining the Appropriate Feature Set for Fish Classification Tasks