Wavelet-modified maximum average correlation height filter for out-of-plane rotation invariance

Goyal, Shilpi; Nishchal, Naveen K.; Beri, Vinod K.; Gupta, A.

doi:10.1016/j.ijleo.2007.01.020

Cited by 6 publications

(7 citation statements)

References 21 publications

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“…The product functions obtained after the use of generated filters in the simulations were displayed on the SLM one by one. 34,35 Figs. 13(a,c,e) show the correlation peaks obtained after using MACH filter.…”

Section: Resultsmentioning

confidence: 99%

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A hybrid digital-optical correlator for automatic target recognition

2007

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Detection of rotationally distorted targets is a challenging task in pattern recognition applications. Recently, we proposed and implemented a wavelet-modified maximum average correlation height (MACH) filter for in-plane and out-of-plane rotation invariance in hybrid digital-optical correlator architecture. Use of wavelet transform improved the performance of the MACH filter by reducing the number of filters required for identifying a rotated target and enhancing the correlation peak intensity significantly. The output of a hybrid digital-optical correlator contains two autocorrelation peaks and a strong dc. To capture a desired single autocorrelation peak a chirp function with the wavelet-modified MACH filter was used. The influence of perturbations in hybrid digital-optical correlator has also been studied. Perturbations include, the effect of occlusion on input target, the effect of additive and multiplicative noise and their combined effect on input target, and the effect of occlusion of product function to be optically processed for obtaining the correlation outputs. The present paper reviews investigations on the hybrid digital-optical correlation scheme with special reference to the work carried out at the Photonics Division, IRDE Dehradun.

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Section: Resultsmentioning

confidence: 99%

“…7(b) shows the simulated correlation outputs with chirp-encoded WaveMACH filter with tank 1 as true class and tanks 4 & 5 as false class. 35 360 images of a tank were captured after rotating the tank out-of-plane at every 1° interval. Figs.…”

Section: Wavelet-modified Mach Filter For In-plane Rotation Invariancementioning

confidence: 99%

A hybrid digital-optical correlator for automatic target recognition

2007

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“…In order to ensure the compensation because of introducing these quantization approaches, quantization error co-efficient ξ is used to control the contribution of quantization error (h eq ) in filter design, where (0 < ξ < 1). Modified Average Image Correlation Height (mAICH) criteria has an additional term h eq for each sample x i because it adds to each sample, as well as in the average of samples, m. Equation (22) presents the mAICH after substitution of v i and m h .…”

Section: Retraining the Cpr Filtermentioning

confidence: 99%

“…Further improvement in the classification accuracy is possible in the Enhanced Eigen Maximum Average Correlation Height Filter (EEMACH) [20] along with a tradeoff. WMACH [21][22][23] enhances the performance of the reference template, and the Gaussian wavelet is applied before training as a pre-processing step. Target search in the input scene enables the CPR techniques to accurately localize the target.…”

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confidence: 99%

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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“…Since then multiple variants including extended MACH (EMACH) [21] and eigen-EMACH (E 2 MACH) [22] have been proposed with respective improvements as well as trade-offs [23]. Most recently some hybrid filtering strategies based on wavelets (WMACH) [24,25] and logarithmic mapping transformations [logarithmically MACH (LMACH)/LEMACH] [26][27][28] have also been proposed to cater for additional problems of in-plane and out-of-plane rotations as well as scale variations.…”

Section: Introductionmentioning

confidence: 99%