In addition, the spatial and gray value differences of pixels should be considered comprehensively to determine their connection matrix. The feature extraction and object segmentation properties of PCNN come from the spike frequency of neurons, and the number of neurons in PCNN is equal to the number of pixels in the input image. From the coupling characteristics of PCNN, junction close space of image and gray level characteristics, it determined the local gray mean square error of image connection strength coefficient. Firstly, it used the improved immune genetic algorithm to adaptively obtain the optimal threshold, and then replaced the dynamic threshold in PCNN model with the optimal threshold, and finally used the pulse coupling characteristics of PCNN model to complete the image segmentation. ![]() In order to solve this problem, this paper proposed an IGA-PCNN image segmentation method combining the improved algorithm and PCNN model. The genetic algorithm improves the loop termination condition and the adaptive setting of model parameters of PCNN image segmentation algorithm, but the PCNN image segmentation algorithm still has the problem of complexity. The PCNN image segmentation method based on genetic algorithm is analyzed. In the simulation process, the initial threshold is optimized by the two-dimensional maximum inter-class variance method, and in order to improve the real-time performance of the algorithm, the fast recurrence formula of neural network is derived and given. The model firstly analyzes the structure and generalization ability of neural network multi-class classifier, uses the minimax criterion of feature space as the splitting criterion of visual perception decision node, which solves the generalization problem of neural network learning algorithm. Based on pulse coupled neural network (PCNN) theory, this paper constructs a visual perception model framework and builds a real image reproduction platform. I also haven't really thought of memory implications for diag using the partition method, although it's clear that as the partition size decreases, memory requirements drop.Pulse-coupled neural networks perform well in many fields such as information retrieval, depth estimation and object detection. Is there a particular reason why this might be prohibitive? For example, it could just perform the i = j indexed operations. (NOTE: The red line represents the loop time as a threshold-it's not to say that the total loop time is constant regardless of the number of loops)įrom the graph it is clear that it takes breaking the operations down into roughly 200x200 square matrices to be faster to use diag than to perform the same operation using loops.Ĭan someone explain why I'm seeing these results? Also, I would think that with MATLAB's ever-more optimized design, there would be built-in handling of these massive matrices within a diag() function call. Xlabel('Log_(Running Time)'), title('Running Time Comparison') Legend('Partioned Running Time', 'Loop Running Time') Plot(log10(fraction), log10(chunkTime), 'g*') % Plot points along time Plot(log10(fraction), repmat(log10(loopTime), 1, length(fraction))) Z(first + 1 : last) = diag(x(first + 1: last) * y(first + 1 : last)') % Dividing the too-large matrix into process-able chunksįraction = ![]() ![]() I decided to test the use of diag() vs a for loop to see if at any point it was more efficient to use diag(): num = 200000 % Matrix dimension In this case, however, MATLAB has to build the entire matrix in order to get the diagonal which causes the memory and speed issues. Because MATLAB is generally optimized for vector/matrix operations, when I first write code, I usually go for the vectorized form. The reason for this was my use of diag() to get the values down the diagonal of an matrix inner product. Time and cause MATLAB to become unresponsive. Creation of arrays greater than this limit may take a long Requested 200000x200000 (298.0GB) array exceeds maximum array size In writing out a matrix operation that was to be performed over tens of thousands of vectors I kept coming across the warning:
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