Automatic Classification of Fish in Underwater Video; Pattern Matching - Affine Invariance and Beyond

Underwater video is used by marine biologists to observe, identify, and quantify living marine resources. Video sequences are typically analyzed manually, which is a time consuming and laborious process. Automating this process will significantly save time and cost. This work proposes a technique for automatic fish classification in underwater video. The steps involved are background subtracting, fish region tracking and classification using features. The background processing is used to separate moving objects from their surrounding environment. Tracking associates multiple views of the same fish in consecutive frames. This step is especially important since recognizing and classifying one or a few of the views as a species of interest may allow labeling the sequence as that particular species. Shape features are extracted using Fourier descriptors from each object and are presented to nearest neighbor classifier for classification. Finally, the nearest neighbor classifier results are combined using a probabilistic-like framework to classify an entire sequence. The majority of the existing pattern matching techniques focus on affine invariance, mainly because rotation, scale, translation and shear are common image transformations. However, in some situations, other transformations may be modeled as a small deformation on top of an affine transformation. The proposed algorithm complements the existing Fourier transform-based pattern matching methods in such a situation. First, the spatial domain pattern is decomposed into non-overlapping concentric circular rings with centers at the middle of the pattern. The Fourier transforms of the rings are computed, and are then mapped to polar domain. The algorithm assumes that the individual rings are rotated with respect to each other. The variable angles of rotation provide information about the directional features of the pattern. This angle of rotation is determined starting from the Fourier transform of the outermost ring and moving inwards to the innermost ring. Two different approaches, one using dynamic programming algorithm and second using a greedy algorithm, are used to determine the directional features of the pattern

Implementation of Directional Median Filtering using Field Programmable Gate Arrays

Median filtering is a non-linear filtering technique which is effective in removing impulsive noise from data. In this thesis, directional median filtering has been implemented using cumulative histogram of samples in several directions. Different methods to implement directional median filtering have been proposed. The filtered images are smoothed along the direction of the filtering window. All implementations aimed to generate outputs in the least amount of time, while reducing the resource utilization on hardware. The implementation methods were designed for Xilinx Virtex 5 FPGA devices but were also attempted on Spartan 3E. The proposed methods used less than 30% of the resources on Virtex 5 FPGA but the resource utilization on Spartan 3E exceeded the number of available resources. After an initial delay, methods 1 and 2 generate a new output for every 5 clock cycles while method 3 generates an output for every 1.5 clock cycles