Dynamic Knowledge Distillation with A Single Stream Structure for RGB-D Salient Object Detection

RGB-D salient object detection(SOD) demonstrates its superiority on detecting in complex environments due to the additional depth information introduced in the data. Inevitably, an independent stream is introduced to extract features from depth images, leading to extra computation and parameters. This methodology which sacrifices the model size to improve the detection accuracy may impede the practical application of SOD problems. To tackle this dilemma, we propose a dynamic distillation method along with a lightweight framework, which significantly reduces the parameters. This method considers the factors of both teacher and student performance within the training stage and dynamically assigns the distillation weight instead of applying a fixed weight on the student model. Extensive experiments are conducted on five public datasets to demonstrate that our method can achieve competitive performance compared to 10 prior methods through a 78.2MB lightweight structure

FFT-based estimation of large motions in images: a robust gradient-based approach

A fast and robust gradient-based motion estimation technique which operates in the frequency domain is presented. The algorithm combines the natural advantages of a good feature selection offered by gradient-based methods with the robustness and speed provided by FFT-based correlation schemes. Experimentation with real images taken from a popular database showed that, unlike any other Fourier-based techniques, the method was able to estimate translations, arbitrary rotations and scale factors in the range 4-6

FFT-based estimation of large motions in images: a robust gradient-based approach

A fast and robust gradient-based motion estimation technique which operates in the frequency domain is presented. The algorithm combines the natural advantages of a good feature selection offered by gradient-based methods with the robustness and speed provided by FFT-based correlation schemes. Experimentation with real images taken from a popular database showed that, unlike any other Fourier-based techniques, the method was able to estimate translations, arbitrary rotations and scale factors in the range 4-6

Fast wavelet-based pansharpening of multi-spectral images

Remote Sensing systems enhance the spatial quality of low-resolution Multi-Spectral (MS) images using information from Pan-chromatic (PAN) images under the pansharpening framework. Most decimated multi-resolution pansharpening approaches upsample the low-resolution MS image to match the resolution of the PAN image. Consequently, a multi-level wavelet decomposition is performed, where the edge information from the PAN image is injected in the MS image. In this paper, the authors propose a pansharpening framework that eliminates the need of upsampling of the MS image, using a B-Spline biorthogonal wavelet decomposition scheme. The proposed method features similar performance to the state-of-the-art pansharpening methods without the extra computational cost induced by upsampling

Fast wavelet-based pansharpening of multi-spectral images

Remote Sensing systems enhance the spatial quality of low-resolution Multi-Spectral (MS) images using information from Pan-chromatic (PAN) images under the pansharpening framework. Most decimated multi-resolution pansharpening approaches upsample the low-resolution MS image to match the resolution of the PAN image. Consequently, a multi-level wavelet decomposition is performed, where the edge information from the PAN image is injected in the MS image. In this paper, the authors propose a pansharpening framework that eliminates the need of upsampling of the MS image, using a B-Spline biorthogonal wavelet decomposition scheme. The proposed method features similar performance to the state-of-the-art pansharpening methods without the extra computational cost induced by upsampling

Automatic Target Recognition Strategy for Synthetic Aperture Radar Images Based on Combined Discrimination Trees

A strategy is introduced for achieving high accuracy in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. Initially, a novel pose rectification process and an image normalization process are sequentially introduced to produce images with less variations prior to the feature processing stage. Then, feature sets that have a wealth of texture and edge information are extracted with the utilization of wavelet coefficients, where more effective and compact feature sets are acquired by reducing the redundancy and dimensionality of the extracted feature set. Finally, a group of discrimination trees are learned and combined into a final classifier in the framework of Real-AdaBoost. The proposed method is evaluated with the public release database for moving and stationary target acquisition and recognition (MSTAR). Several comparative studies are conducted to evaluate the effectiveness of the proposed algorithm. Experimental results show the distinctive superiority of the proposed method under both standard operating conditions (SOCs) and extended operating conditions (EOCs). Moreover, our additional tests suggest that good recognition accuracy can be achieved even with limited number of training images as long as these are captured with appropriately incremental sample step in target poses

Towards Automated Polyp Segmentation Using Weakly- and Semi-Supervised Learning and Deformable Transformers

Polyp segmentation is a crucial step towards computer-aided diagnosis of colorectal cancer. However, most of the polyp segmentation methods require pixel-wise annotated datasets. Annotated datasets are tedious and time-consuming to produce, especially for physicians who must dedicate their time to their patients. We tackle this issue by proposing a novel framework that can be trained using only weakly annotated images along with exploiting unlabeled images. To this end, we propose three ideas to address this problem, more specifically our contributions are: 1) a novel sparse foreground loss that suppresses false positives and improves weakly-supervised training, 2) a batch-wise weighted consistency loss utilizing predicted segmentation maps from identical networks trained using different initialization during semi-supervised training, 3) a deformable transformer encoder neck for feature enhancement by fusing information across levels and flexible spatial locations. Extensive experimental results demonstrate the merits of our ideas on five challenging datasets outperforming some state-of-the-art fully supervised models. Also, our framework can be utilized to fine-tune models trained on natural image segmentation datasets drastically improving their performance for polyp segmentation and impressively demonstrating superior performance to fully supervised fine-tuning

Salient Object Detection Combining a Self-Attention Module and a Feature Pyramid Network

Funding This research was funded by the EU H2020 TERPSICHORE project “Transforming Intangible Folkloric Performing Arts into Tangible Choreographic Digital Objects” under the grant agreement 691218.Peer reviewedPublisher PD