Interpreting image databases by region classification

This disclosure provides vehicle detection methods and systems including irrelevant search window elimination and/or window score degradation. According to one exemplary embodiment, provided is a method of detecting one or more parked vehicles in a video frame, wherein candidate search windows are limited to one or more predefined window shapes. According to another exemplary embodiment, the method includes degrading a classification score of a candidate search window based on aspect ratio, window overlap area and/or a global maximal classification.

Vehicle detection method and system including irrelevant window elimination and/or window score degradation

The main aim of this thesis is to evaluate the performance of image segmentation and texture analysis algorithms on synthetic and real images. As a part of this study, two popular texture benchmarks called MeasTex and VisTex have been used. A new scene analysis benchmark, called PANN database, has been generated as a part of this study for the evaluation of image analysis tools on natural object recognition tasks. The thesis demonstrates the considerable variability in an image understanding system performance based on different choices of image segmentation and texture analysis algorithms used. Hence, it is proposed that optimising each image analysis tool in a chain of processes is not enough for deriving optimal performances. The effect of a preceding process on its successor is both important and significant. So for example, how well we segment images, can have a dramatic impact on the quality of texture features we extract from such regions and subsequently this impacts our object recognition ability. This thesis includes results of exhaustive experimentation with four image segmentation algorithms, five texture analysis algorithms, and two classifiers, to demonstrate this variability. The thesis also discusses the similarity and differences in performances highlighting different patterns of mistakes made by different combinations. In addition to being a comparative study, it also shows results on object recognition in natural scene images. A complete system starting from image acquisition to generating ground truth data and classifying it is described for the analysis of natural scenes.

/pdf/performance-evaluation-of-image-segmentation-and-texture-3w76j5laez.pdf

Performance Evaluation of Image Segmentation and Texture Extraction Methods in Scene Analysis

Flexible view recognition for indoor navigation based on Gabor filters and support vector machines

In this paper we apply artificial neural networks for classifying texture data of various natural objects found in FLIR images. Hermite functions are used for texture feature extraction from segmented regions of interest in natural scenes taken as a video sequence. A total of 2680 samples for a total of twelve different classes are used for object recognition. The results on correctly identifying twelve natural objects in scenes are compared across ten folds of the cross-validation study. Neural networks are found to be extremely effective in robust classification of our data giving an average recognition rate of 91.8%.

Natural object classification using artificial neural networks

Integrating Spatio-temporal Information in Image Sequence Analysis to Enforce Consistency of Interpretation

Perceptual organisation can be defined as the ability to impose structural organisation on sensory data, so as to group sensory primitives arising from a common underlying cause. Our organisational philosophy is hierarchical, with complex organisations being formed from simpler ones. In this paper, directional features extracted from Gabor responses are used as the primitives for perceptual grouping. In previous work, we extracted Gabor features in 8 directions and then applied two SOMs, thus classifying each pixel in the image within a 8x10 neuronmap, each corner of which represents one of four main directions, (horizontal, vertical, left diagonal and right diagonal). In the present work we group pixels with similar directional features, thereby detecting salient structures within an image. These detected-structures will be used as tokens from which to create the next level of abstraction in the hierarchy of the system. This approach is an alternative to the use of sets of edges as primary features: the directional features that Gabor filters provide are a potentially richer source of information. Preliminary results obtained from application to forward-looking infrared (FLIR) images are very promising. At present only four main directions are utilised, i.e. vertical, horizontal, right diagonal and left diagonal: the technique may be readily extended to the eight utilised in previous work. The next stage will be to group the tokens by the application of additional Gestalt-laws in order to detect objects.

http://www.bmva.org/bmvc/1998/pdf/p164.pdf

Perceptual Grouping from Gabor Filter Responses

Texture can be interpreted as a measure of the 'edginess' about a pixel and can thus be described by edge co-occurrence matrices. The matrix can be decomposed using 2-dimensional orthogonal Hermite functions, the coefficients of which provide a low order feature vector which is characteristic of the texture. The Hermite coefficients for 240 hand-segmented regions of grass, trees, sky and river from 60 forward looking infrared (FLIR) images have been used to train and validate 2 neural networks, which have subsequently been used to label FLIR images segmented using co-occurrence techniques [1].

/pdf/neural-networks-for-the-texture-classification-of-segmented-4chn0ji62q.pdf

Neural Networks for the Texture Classification of Segmented Regions of Forward Looking Infrared Images

Adaptive dynamic scene analysis

A cooccurrence space is defined by utilising the combinations of pixel strengths defined by a Canny edge operator. A region and boundary segmentation derived from this space is first edge thinned by non-maximal suppression and then hysteresis is used as a post-processing step to improve the edges. The distributions in cooccurrence space define the thresholds employed in the hysteresis post-processing.

/pdf/simultaneous-region-and-edge-segmentation-of-infrared-images-1o60d408zk.pdf

Simultaneous region and edge segmentation of infrared images using non-maximal suppression for edge thinning.

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