Despite recent improvements in computer vision, artificial visual systems’ design is still daunting since an explanation of visual computing algorithms remains elusive. Salient object detection is one problem that is still open due to the difficulty of understanding the brain’s inner workings. Progress in this research area follows the traditional path of hand-made designs using neuroscience knowledge or, more recently, deep learning, a particular branch of machine learning. Recently, a different approach based on genetic programming appeared to enhance handcrafted techniques following two different strategies. The first method follows the idea of combining previous hand-made methods through genetic programming and fuzzy logic. The second approach improves the inner computational structures of basic hand-made models through artificial evolution. This research proposes expanding the artificial dorsal stream using a recent proposal based on symbolic learning to solve salient object detection problems following the second technique. This approach applies the fusion of visual saliency and image segmentation algorithms as a template. The proposed methodology discovers several critical structures in the template through artificial evolution. We present results on a benchmark designed by experts with outstanding results in an extensive comparison with the state of the art, including classical methods and deep learning approaches to highlight the importance of symbolic learning in visual saliency.

Machine learning is at the center of mainstream technology and outperforms classical approaches to handcrafted feature design. Aside from its learning process for artificial feature extraction, it has an end-to-end paradigm from input to output, reaching outstandingly accurate results. However, security concerns about its robustness to malicious and imperceptible perturbations have drawn attention since its prediction can be changed entirely. Salient object detection is a research area where deep convolutional neural networks have proven effective but whose trustworthiness represents a significant issue requiring analysis and solutions to hackers' attacks. Brain programming is a kind of symbolic learning in the vein of good old-fashioned artificial intelligence. This work provides evidence that symbolic learning robustness is crucial in designing reliable visual attention systems since it can withstand even the most intense perturbations. We test this evolutionary computation methodology against several adversarial attacks and noise perturbations using standard databases and a real-world problem of a shorebird called the Snowy Plover portraying a visual attention task. We compare our methodology with five different deep learning approaches, proving that they do not match the symbolic paradigm regarding robustness. All neural networks suffer significant performance losses, while brain programming stands its ground and remains unaffected. Also, by studying the Snowy Plover, we remark on the importance of security in surveillance activities regarding wildlife protection and conservation.

Enhancement tasks for low-light images are an open problem whose solutions can impact downstream tasks in fields like surveillance, medicine, autonomous driving, or object detection. As a result, the necessity to create algorithms that can withstand harmful and subtle disturbances has garnered interest, as both humans and machines can significantly influence how programs perform. Enhancing images in low-light conditions is a field of research where deep convolutional neural networks have shown considerable effectiveness. Nevertheless, the reliability of these systems poses a significant challenge that needs examination and remedies against hacking attempts. This image repository dataset evaluates adversarial robustness by introducing 12 examples, each leveraging a known adversarial attack or noise perturbation. The repository contains a total of 4970 digital images through the application of adversarial examples to five different image databases (i.e., MEF, LIME, LOLv1, LOLv2-R, LOLv2-S) that include pictures of landscapes, objects, or locations with low, normal, or high illumination conditions. We incorporate original images from the five databases utilized alongside the adversarial examples to facilitate easy access and distribution of this dataset.

Despite recent improvements in computer vision, artificial visual systems' design is still daunting since an explanation of visual computing algorithms remains elusive. Salient object detection is one problem that is still open due to the difficulty of understanding the brain's inner workings. Progress on this research area follows the traditional path of hand-made designs using neuroscience knowledge. In recent years two different approaches based on genetic programming appear to enhance their technique. One follows the idea of combining previous hand-made methods through genetic programming and fuzzy logic. The other approach consists of improving the inner computational structures of basic hand-made models through artificial evolution. This research work proposes expanding the artificial dorsal stream using a recent proposal to solve salient object detection problems. This approach uses the benefits of the two main aspects of this research area: fixation prediction and detection of salient objects. We decided to apply the fusion of visual saliency and image segmentation algorithms as a template. The proposed methodology discovers several critical structures in the template through artificial evolution. We present results on a benchmark designed by experts with outstanding results in comparison with the state-of-the-art.

Three-dimensional (3D) reconstruction from a photogrammetric perspective aims to infer the geometric structure of a scene from a set of images, including the recovery of depth information inherently lost during image acquisition. Conventional photogrammetric pipelines rely on multiple handcrafted processing stages, often requiring manual intervention. This work introduces a dataset designed to support the study of background removal techniques in photogrammetric workflows through salient object detection (SOD). The dataset comprises 15,120 images divided into sets of 28 distinct objects, each set including 36 high-resolution RGB images captured from multiple viewpoints. Additionally, each set provides 36 manually segmented images, as well as automatically segmented versions obtained using four different SOD algorithms. To facilitate evaluation and reproducibility, 153 reconstructed 3D models are provided across all object categories, and a 3D reconstruction evaluation methodology based on the Chamfer Distance metric is proposed, enabling the analysis of the impact of different segmentation strategies on 3D reconstruction. The dataset offers a benchmark resource for the development, comparison, and validation of methods aimed at improving photogrammetric pipelines through automated information filtering.