Hardware design of correlation filters for target detection

Akbar, Naeem; Tehsin, Sara; Rehman, Haseeb ur; Rehman, Saad; Young, Rupert

doi:10.1117/12.2519497

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…This ratio estimates the quality after quantization. Equation (33) represents the PSNR, while the power of noise in the denominator is defined by the Mean Square Error (MSE), which is the average of the square of the pixel-by-pixel difference between the original image and the approximated version of the image; however, the MSE also depends on the variance of the original and quantized signals. Equation (34) establishes a relationship between MSE and variance of the signal.…”

Section: Geometric Transformmentioning

confidence: 99%

“…The proposed methodology pre-processes the color conversion approach and background elimination to enhance correlation filters' speed and accuracy. Akbar et al [33] propose the hardware implementation of correlation filters on FPGA, which reduces the processing time with negligible performance loss. Hardware design is implemented in LabView, which later may be used later in real-time security applications.…”

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confidence: 99%

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Weight Quantization Retraining for Sparse and Compressed Spatial Domain Correlation Filters

et al. 2021

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Using Spatial Domain Correlation Pattern Recognition (CPR) in Internet-of-Things (IoT)-based applications often faces constraints, like inadequate computational resources and limited memory. To reduce the computation workload of inference due to large spatial-domain CPR filters and convert filter weights into hardware-friendly data-types, this paper introduces the power-of-two (Po2) and dynamic-fixed-point (DFP) quantization techniques for weight compression and the sparsity induction in filters. Weight quantization re-training (WQR), the log-polar, and the inverse log-polar geometric transformations are introduced to reduce quantization error. WQR is a method of retraining the CPR filter, which is presented to recover the accuracy loss. It forces the given quantization scheme by adding the quantization error in the training sample and then re-quantizes the filter to the desired quantization levels which reduce quantization noise. Further, Particle Swarm Optimization (PSO) is used to fine-tune parameters during WQR. Both geometric transforms are applied as pre-processing steps. The Po2 quantization scheme showed better performance close to the performance of full precision, while the DFP quantization showed further closeness to the Receiver Operator Characteristic of full precision for the same bit-length. Overall, spatial-trained filters showed a better compression ratio for Po2 quantization after retraining of the CPR filter. The direct quantization approach achieved a compression ratio of 8 at 4.37× speedup with no accuracy degradation. In contrast, quantization with a log-polar transform is accomplished at a compression ratio of 4 at 1.12× speedup, but, in this case, 16% accuracy of degradation is noticed. Inverse log-polar transform showed a compression ratio of 16 at 8.90× speedup and 6% accuracy degradation. All the mentioned accuracies are reported for a common database.

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Section: Geometric Transformmentioning

confidence: 99%

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confidence: 99%

Weight Quantization Retraining for Sparse and Compressed Spatial Domain Correlation Filters

et al. 2021

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“…However, a significant hurdle faced by AI-supported systems is their perceived nature as computational" black boxes. " The lack of transparency in the decision-making processes of these AI models has resulted in hesitancy among healthcare institutions when it comes to adopting them for diagnostic purposes, despite their effectiveness (24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35). It is therefore important for AI researchers to integrate digestible explanations throughout the development of AI-aided medical applications, thus assuring healthcare practitioners while also clearing any doubts they might have.…”

Section: Introductionmentioning

confidence: 99%

Explainable AI-driven model for gastrointestinal cancer classification

Binzagr

2024

Front. Med.

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Although the detection procedure has been shown to be highly effective, there are several obstacles to overcome in the usage of AI-assisted cancer cell detection in clinical settings. These issues stem mostly from the failure to identify the underlying processes. Because AI-assisted diagnosis does not offer a clear decision-making process, doctors are dubious about it. In this instance, the advent of Explainable Artificial Intelligence (XAI), which offers explanations for prediction models, solves the AI black box issue. The SHapley Additive exPlanations (SHAP) approach, which results in the interpretation of model predictions, is the main emphasis of this work. The intermediate layer in this study was a hybrid model made up of three Convolutional Neural Networks (CNNs) (InceptionV3, InceptionResNetV2, and VGG16) that combined their predictions. The KvasirV2 dataset, which comprises pathological symptoms associated to cancer, was used to train the model. Our combined model yielded an accuracy of 93.17% and an F1 score of 97%. After training the combined model, we use SHAP to analyze images from these three groups to provide an explanation of the decision that affects the model prediction.

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