The field of signal processing and computer vision is witnessing significant developments, with a focus on improving the accuracy and efficiency of various algorithms and techniques. Researchers are exploring new methods for estimating camera spectral sensitivity, detecting outliers in circle fitting applications, and enhancing diameter measurement accuracy in machine vision systems. Additionally, there is a growing interest in developing novel divergence measures and optimizing discrepancy criteria, which have far-reaching implications for signal processing and information sciences. Notably, the introduction of new divergence measures, such as the alpha-beta divergence for complex data and the average squared discrepancy, is advancing the field. The development of innovative approaches, such as the Polar Coordinate-Based Outlier Detection algorithm and the conversion factor-based method for enhancing measurement accuracy, is also noteworthy. Some particularly noteworthy papers include: The alpha-beta divergence for real and complex data, which extends the definition of alpha-beta divergences to accommodate complex data. The paper On the optimization of discrepancy measures introduces the average squared discrepancy, which avoids the problems raised by traditional discrepancy measures.