RESUMEN
One of the essential abilities in animals is to detect novelties within their environment. From the computational point of view, novelty detection consists of finding data that are different in some aspect to the known data. In robotics, researchers have incorporated novelty modules in robots to develop automatic exploration and inspection tasks. The visual sensor is one of the preferred sensors to perform this task. However, there exist problems as illumination changes, occlusion, and scale, among others. Besides, novelty detectors vary their performance depending on the specific application scenario. In this work, we propose a visual novelty detection framework for specific exploration and inspection tasks based on evolved novelty detectors. The system uses deep features to represent the visual information captured by the robots and applies a global optimization technique to design novelty detectors for specific robotics applications. We verified the performance of the proposed system against well-established state-of-the-art methods in a challenging scenario. This scenario was an outdoor environment covering typical problems in computer vision such as illumination changes, occlusion, and geometric transformations. The proposed framework presented high-novelty detection accuracy with competitive or even better results than the baseline methods.
RESUMEN
In this paper, I propose a new unsupervised change detection method for optical satellite imagery. The proposed technique consists of three phases. In the first stage, difference images are calculated using four different functions. Two of the functions were first used in this study. In the second stage, using Reconstruction Independent Component Analysis, this four-difference matrix is projected to one feature. In the last stage, clustering is performed. Kmeans tuned by Artificial Bee Colony (ABC-Kmeans) clustering technique has been developed and proposed by following a different strategy in the clustering phase. The effectiveness of the proposed approach was examined using two different datasets, Sardinia and Mexico. Quantitative evaluation was performed in two stages. In the first stage, proposed method was compared with different unsupervised change detection algorithms using False Alarm, Missed Alarm, Total Error, and Total Error Rate metrics which are calculated using ground truth image in dataset. In the second experimental study, the proposed approach is compared in detail with PCA-Kmeans approach, which is quite often preferred for similar studies, using the Mean Squared Error, Peak Signal to Noise Ratio, Structural Similarity Index, and Universal Image Quality Index metrics. According to quantitative and qualitative analysis, proposed approach can produce quite successful results using optical remote sensing data.