The color histogram is a statistical behavior for robust pattern search or matching; however, difficulties have arisen in using it to discriminate among similar objects. Our method, called absent color indexing (ABC), describes how to use absent or minor colors as a feature in order to solve problems while robustly recognizing images, even those with similar color features. The proposed approach separates a source color histogram into apparent (AP) and absent (AB) color histograms in order to provide a fair way of focusing on the major and minor contributions together. A threshold for this separation is automatically obtained from the mean color histogram by considering the statistical significance of the absent colors. After these have been separated, an inversion operation is performed to reinforce the weight of AB. In order to balance the contributions of the two histograms, four similarity measures are utilized as candidates for combination with ABC. We tested the performance of ABC in terms of the F-measure using different similarity measures, and the results show that it is able to achieve values greater than 0.95. Experiments on Mondrian random patterns verify the ability of ABC to distinguish similar objects by margin. The results of extensive experiments on real-world images and open databases are presented here in order to demonstrate that the performance of our relatively simple algorithm remained robust even in difficult cases.

Color is an essential feature in histogram-based matching. This can be extracted as statistical data during the comparison process. Although the applicability of color features in histogram-based techniques has been proven, position information is lacking during the matching process. We present a conceptually simple and effective method called multiple-layered absent color indexing (ABC-ML) for template matching. Apparent and absent color histograms are obtained from the original color histogram, where the absent colors belong to low-frequency or vacant bins. To determine the color range of compared images, we propose a total color space (TCS) that can determine the operating range of the histogram bins. Furthermore, we invert the absent colors to obtain the properties of these colors using threshold $h_{\mathrm{T}}$. Then, we compute the similarity using the intersection. A multiple-layered structure is proposed against the shift issue in histogram-based approaches. Each layer is constructed using the isotonic principle. Thus, absent color indexing and multiple-layered structure are combined to solve the precision problem. Our experiments on real-world images and open data demonstrated that they have produced state-of-the-art results. Moreover, they retained the histogram merits of robustness in cases of deformation and scaling.

This paper presents a novel method that absorbs the strong discriminative ability from absent color indexing (ABC) to enhance sensitivity and combines it with a correlation filter (CF) for obtaining a higher precision; this method is named ABC-CF. First, by separating the original color histogram, apparent and absent colors are introduced. Subsequently, an automatic threshold acquisition is proposed using a mean color histogram. Next, a histogram intersection is selected to calculate the similarity. Finally, CF follows them to solve the drift caused by ABC during the matching process. The novel approach proposed in this paper realizes robustness in distortion of target images and higher margins in fundamental matching problems, and then achieves more precise matching in positions. The effectiveness of the proposed approach can be evaluated in the comparative experiments with other representative methods by use of the open data.

Background: Monocular depth estimation in endoscopic scenes is a fundamental prerequisite for intraoperative 3D reconstruction and surgical navigation. However, factors such as weak textures, specular reflections, local overexposure, and dynamic illumination changes in endoscopic environments can undermine the effectiveness of photometric-consistency-based self-supervised learning methods, leading to insufficient structural representation and limited prediction accuracy. Methods: To address this issue, this paper presents a task-specific architectural extension of the EndoDAC framework for self-supervised monocular endoscopic depth estimation. Specifically, the proposed method adapts and integrates geometry- and illumination-aware feature enhancement, offline multi-generation self-distillation, and inference-stage structural fusion to improve feature representation, prediction refinement, and deployment efficiency under weak-texture and adverse illumination conditions. Experiments are conducted on the SCARED dataset for training and in-domain evaluation, while zero-shot cross-domain testing is performed on the Hamlyn dataset. Results: The results show that the proposed EndoDAC-based extension improves depth estimation performance on the SCARED dataset and achieves lower error-oriented metrics than the baseline on Hamlyn zero-shot evaluation, with threshold accuracy remaining comparable to the baseline. Conclusions: The proposed method demonstrates that task-specific adaptation of geometry- and illumination-aware feature enhancement, offline self-distillation, and inference-stage fusion can improve an EndoDAC-based self-supervised endoscopic depth estimation pipeline. These results support the effectiveness of the proposed architectural adaptation, while also indicating that further work is needed to improve cross-domain scale consistency and robustness under more challenging surgical conditions.