This letter presents a low-complexity attention module for fast change detection. The proposed module computes the absolute difference between bi-temporal features extracted by a Siamese backbone network and sequentially applies spatial and channel attention to generate key change representations. Spatial attention emphasizes important spatial locations using representative values from channel-wise pooling, while channel attention highlights discriminative feature responses using values from spatial-wise pooling. By leveraging low-dimensional representative features, the module significantly reduces computational cost. Additionally, its dual-attention structure—driven by feature differences—enhances both spatial localization and semantic relevance of changes. Compared to the Change-Guided Network (CGNet), the proposed method reduces multiply-accumulate operations (MACs) by 53.81% with only a 0.15% drop in F1-score, demonstrating high efficiency with minimal performance degradation. These results suggest that the proposed method is suitable for large-scale or real-time remote sensing applications where computational efficiency is essential.

Spiking neural networks (SNNs) have been recently highlighted as an attractive approach for implementing artificial intelligence models in resource-constrained edge devices for various industrial applications. These SNNs leverage low-power and wide dynamic range processing through biologically inspired event-driven operations in a massively parallel manner. In this respect, we propose BCRNet-SNN, an SNN model designed to utilize an electric body channel response (BCR) as a biometric feature for user recognition, where each element in the BCR dataset for 15 subjects is a 1-D vector comprising 380 feature points from the measured envelope by applying chirp signals to the body. The network parameters for implementing the posttrained BCRNet-SNN are inherited from the proposed convolutional neural network (CNN) that extensively extracts BCR-based biometric features (BCRNet), followed by applying knowledge distillation-based network lightening process on BCRNet. The performance evaluation results compared to ResNet18 and ResNet6 for the BCR dataset show that BCRNet achieves a greater than 2$\%$and 1.4$\%$improvement, respectively, in the average classification accuracy, while significantly reducing the number of network parameters to less than 1$\%$. The student network distilled from the teacher BCRNet (BCRNet-S) requires only 5.1$\%$network parameters than BCRNet, which can be eligible for implementation in BCRNet-SNN. The proposed structure of BCRNet-SNN to effectively accommodate the CNN-to-SNN-converted parameters from BCRNet-S can achieve up to a maximum accuracy of 98.11$\%$, without observable performance degradation compared to BCRNet.

Video-based dynamic mesh compression is being developed for a standard for compressing time-varying mesh data by using existing 2D video codecs. In this paper, we propose a spatial scalable structure for video-based dynamic mesh compression to support scalable applications of the dynamic mesh coding. To support the spatial scalable functionality for geometry, we propose a level of detail based displacement frame packing method that enables decoding up to a certain level. For texture scalable coding, multi-layer texture map is generated from high resolution texture map, and then the scalable high efficiency video coding (SHVC) is employed to remove redundancy between layers. The experimental results demonstrate that our proposed scalable coding method can improve the BD-rate in a 3-layer scalable configuration.

Rate control is an important component of an end-to-end video communication system. Currently, there are several proposals for rate control in the upcoming High Efficiency Video Coding (HEVC) standard, but none specifically for the multi-view extension of the standard. In this paper, we apply one of the HEVC single-view rate control schemes to the multi-view scenario, and propose two improvements. One improvement deals with Quantization Parameter (QP) initialization, and the other deals with interview Mean Absolute Difference (MAD) prediction. Experimental results demonstrate increased accuracy of rate control, reduced fluctuation of instantaneous bitrate, as well as a reduction in PSNR degradation compared to the existing rate control algorithm.

Load balancing algorithm supporting parallel tools for HEVC encoder is proposed in this paper. Standardization of HEVC version 1 was finalized and which is known that its RD performance is two times better than H.264/AVC which was the most efficient video coder. However, computational complexity of HEVC encoding process derived from variable block sizes based on hierarchical structure and recursive encoding structure should be dealt as a prerequisite for technique commercialization. In this paper, basic performances of slice- and tile-level parallel tools adopted in HEVC are firstly presented and load balancing algorithm based on complexity model for slices and tiles is proposed. For four slices and four tiles cases, average time saving gains are 12.05% and 3.81% against simple slice- and tile-level parallelization, respectively.

In this paper, we propose a new distributed video coding (DVC) method based on adaptive slice size using received motion vectors (MVs). In the proposed algorithm, the MVs estimated at a DVC decoder are transmitted to a corresponding encoder. In the proposed encoder, a predicted side information (PSI) is reconstructed with the transmitted MVs and key frames. Therefore, the PSI can be generated same to side information (SI) at the decoder. We can, also, calculate an exact crossover probability between the SI and original input frame using PSI and the original frame. As a result, the proposed method can transmit minimum parity bits to maximize error correction ability of a channel decoder with minimal computational complexity. Experimental results show that the proposed algorithm is better than several conventional DVC methods.

Presents an integrated system for navigation parameter estimation using sequential aerial images, where the navigation parameters represent the positional and velocity information of an aircraft for autonomous navigation. The proposed integrated system is composed of two parts: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current position of an aircraft by accumulating relative displacement estimates extracted from two successive aerial images. Simple accumulation of parameter values reduces the reliability of the extracted parameter estimates as an aircraft goes on navigating, resulting in a large positional error. Therefore, absolute position estimation is required to compensate for the positional error generated by the relative position estimation. Absolute position estimation algorithms using image matching and digital elevation model (DEM) matching are presented. In the image matching, a robust-oriented Hausdorff measure (ROHM) is employed, whereas in the DEM matching, an algorithm using multiple image pairs is used. Experiments with four real aerial image sequences show the effectiveness of the proposed integrated position estimation algorithm.

This paper proposes the localization algorithm that estimates translation parameters of an aircraft by comparing the sampled elevation map recovered from aerial sequence images, and the digital elevation model (DEM) with the given orientation and altitude parameters obtained from a gyroscope. It consists of two stages: recovering the sampled elevation map from multiple aerial image pairs and matching of the relative recovered elevation map (REM) with the relative DEM. Computer simulations with three real aerial image sequences show the effectiveness of the proposed algorithm.

This paper proposes an oriented Hausdorff similarity (OHS) measure for robust object alignment. The OHS measure is introduced by replacing the distance concept of conventional Hausdoff distance (HD) algorithms by the similarity concept of the Hough transform (HT). The proposed algorithm can be considered as the modified directed HT using the distance transform (DT). The orientation information at each pixel is also used to remove incorrect correspondences.

A texture descriptor invariant to translation, rotation and scale changes is presented in this paper. A power spectral image is obtained by using the DFT to extract the proposed descriptor for the translation invariance of a given texture. The power spectral image is scale-normalized by a cut-off frequency on the power spectral image that is calculated as the total energy inside a circle with its radius of the cut-off frequency amounts to a predetermined value. Finally, the rotation invariant Zernike moments are calculated on the translation and scale normalized image for a rotation, scale and translation invariant descriptor. The extraction is simple and fast, using fast known DFT and Zernike transformation. The matching is also simple and fast using the Euclidean distance measure between a query and test textures. The effectiveness of the proposed algorithm is shown with various texture databases.

This paper proposes two Hausdorff distance (HD) matching algorithms, in which robust HD measures are implemented in pyramidal structures. By computer simulations, the matching performance of the conventional HD measures and the proposed robust HD matching algorithms using pyramidal structures is compared, with real images which are degraded by noise and occlusions.

This work presents a hybrid method for navigation parameter estimation using sequential aerial images, where navigation parameters represent the position and velocity information of an aircraft for autonomous navigation. The proposed hybrid system is composed of two parts: relative position estimation and absolute position estimation. Computer simulation with two different sets of real aerial image sequences shows the effectiveness of the proposed hybrid parameter estimation algorithm.

A Hausdorff distance (HD) is one of commonly used measures for object matching. This work analyzes the conventional HD measures and proposes two robust HD measures based on m-estimation and least trimmed square (LTS) which are more efficient than the conventional HD measures. By computer simulation, the matching performance of the conventional and proposed HD measures is compared with synthetic and real images.

This paper proposes a motion estimation algorithm that is robust to motion discontinuity and noise. The proposed algorithm is constructed by embedding the least median squares (LMedS) of robust statistics into the maximum a posteriori (MAP) estimator. Difficulties in accurate estimation of the motion field arise from the smoothness constraint and the sensitivity to noise. To cope robustly with these problems, a median operator and the concept of reweighted least squares (RLS) are applied to the MAP motion estimator, resulting in the reweighted robust MAP (RRMAP). The proposed RRMAP motion estimation algorithm is also generalized for multiple image frame cases. Computer simulation with various synthetic image sequences shows that the proposed algorithm reduces errors, compared to three existing robust motion estimation algorithms that are based on M-estimation, total least squares (TLS), and Hough transform. It is also observed that the proposed algorithm is statistically efficient and robust to additive Gaussian noise and impulse noise. Furthermore, the proposed algorithm yields reasonable performance for real image sequences.

This paper proposes a multiresolution surface parameter estimation method for range images. Based on the robust estimation, it approximates a patch to a planar or quadric surface in the locally adaptive window. Selection of resolution is made pixelwise by comparing a locally computed homogeneity measure with the global threshold calculated by the distribution of the approximation error. Experimental results show that the proposed multiresolution method well preserves step and roof edges and is robust to noise compared with the conventional parameter estimation methods.

A Hausdorff distance (HD) is one of commonly used measures for object matching. It calculates the distance between two point sets of edges in two-dimensional binary images without establishing correspondences. This paper analyzes the conventional HD measures based on robust statistics, and proposes robust HD measures based on M-estimation, least trimmed square (LTS), and /spl alpha/-trimmed mean methods. The matching performance by the conventional and proposed HD measures is compared with synthetic and real images.

This paper presents a method for navigation parameter estimation using sequential aerial images, where navigation parameters represent the velocity and position information of an aircraft for autonomous navigation. The proposed navigation parameter estimation system is composed of two parts: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current velocity and position of an aircraft by accumulating navigation parameters extracted from two successive aerial images. However, simple accumulation of parameter values decreases reliability of the extracted parameters as an aircraft goes on navigating, resulting in a large position error. Therefore absolute position estimation is required to compensate for position error generated in the relative position estimation step. A hybrid absolute position estimation algorithm combining image matching and digital elevation model (DEM) matching is presented. In image matching, line segment matching or Hausdorff distance (HD) matching is employed whereas in DEM matching a new algorithm for absolute position estimation by minimizing the variance of displacements is proposed. Computer simulation with real aerial image sequences shows the effectiveness of the proposed algorithm.