Visualisations are essential for communicating lighting design and are an elemental part of disseminating lighting-related research. The topic is closely connected to other topics, such as visual perception and cognitive decision-making. Designing visualisations that effectively and transparently communicate often complex data typically requires skills in several areas and is often argued to be both an art and a science. In the increasingly digital world and the continual digitalisation of the Architectural, Engineering and Construction industry, more data are available and needed in the built environment. For example, the concept of smart buildings, which depend on sensors and communication of subsystems to dynamically control various systems and features within the building, is evolving rapidly. Moreover, the availability and development of digital twins are expanding fast. Using real and manipulated images combined with relevant literature, this perspective article aims to raise awareness of a crucial topic and to guide future research. Concentrating on lighting-related data, this article addresses two aspects of information visualisation: using colour to visualise information and creating interactive visualisations.

With the growing frequency and diversity of threats targeting critical infrastructure, rapid and reliable intrusion detection has become essential. While video surveillance systems such as CCTV are widely used, radar-based solutions offer key advantages, including long-range detection and robustness to poor visibility conditions. Among these, micro-Doppler (mD) signature classification has gained significant attention, primarily focusing on binary human detection using either feature-based or deep learning approaches. However, such methods often lack generalizability and adaptability to complex environments or diverse object types. This study introduces a hybrid knowledge-based framework for mD signature interpretation. The approach commences with a morphological decomposition of mD signals in outdoor environments, thereby enabling the identification of sub-signal components.

Social networks like LinkedIn, Facebook, and Instagram contribute significantly to the rise of image prevalence in daily life, with numerous images posted in everyday. Detecting image similarity is crucial for many applications. While deep learning methods like Learned Perceptual Image Patch Similarity (LPIPS) are popular, they often overlook image structure. An alternative method involves using pre-trained models (e.g., LeNet-5 and VGG-16) to extract features and employing classifiers. However, deep learning methods demand substantial computational resources and they also suffer from uncontrolled false alarms. This paper proposes a novel Generalized Likelihood Ratio Test (GLRT) detector based on a hypothesis testing framework to identify the similarity of structural image pairs. The proposed approach minimizes the need for extensive computational resources, and false alarms can be regulated by employing a threshold. The detector is applied to Local Dissimilarity Maps (LDM), with gray-level values modeled by a statistical distribution. Experimental results on simulated and real data confirm its effectiveness for structural similarity detection. Additionally, a Simple Likelihood Ratio Test (SLRT) is tested on simulated data. Comparisons with deep learning and classical measures like Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) show the proposed detector performs comparably or better in terms of Area Under the Curve (AUC) with less computing time, especially for structural similarity.

Commercially available presence sensing technologies for smart home lighting are often driven by energy efficiency. However, they struggle to accommodate user's dynamic behaviours, especially in small houses where multiple activities overlap within compact layouts. Small houses, with mixed-function spaces require sensors that can accurately detect short-range or overlapping activity zones, calling for more user-centric presence sensing that is responsive to user's behavioural needs and energy efficient. An exploratory study was conducted in a test facility simulating a small house, investigating optimal sensor placement to improve presence detection and minimise unnecessary lighting in unoccupied zones. It examined the performance of 2 conventional and 12 short-range radar sensors, positioned at varying heights and angles. Radar's potential as a non-wearable sensing solution for health monitoring was also explored. Seventy participants performed three tasks, representing active, sedentary and passive behaviours, under controlled home lighting conditions. Results reinforced that optimal sensor placement in a small house enhanced user presence detection and minimised false triggers. Compared to the conventional sensors, radar-based micro-location sensing presented prospects for accommodating dynamic user behaviours and overlapping activity zones in small houses. It enabled localised lighting based on fine-grain user activity recognition from the respiratory signal, thereby reducing unnecessary lighting energy usage. Notably, it synchronised lighting with user's 'sleep-state' activity and offered a non-wearable solution to monitor user's health via vital sign detection. These findings lay the foundation for developing adaptive and responsive user-centric home lighting systems for small houses and assistive living environments.

In this paper, an original method for texture comparison and classification is presented. It is based on an adaptation of the Gray Local Dissimilarity Map (GLDM) for the comparison of textured images. In our method, GLDM of gray level co-occurrence matrices (GLCM) are computed instead of GLDM of images directly. Only one parameter is extracted from these GLDMs and used to classify textures. The method is tested on a texture dataset with two-class and multi-class classification using K-Nearest Neighbours (KNN) to prove its efficiency. The obtained results show that computing the GLDM of GLCM gives better performance for texture classification than both computing the GLDM of images and classification methods based on the extraction of several characteristics from GLCM.

Current presence-sensing technologies for energy-efficient lighting control and building optimisation are (i) catered to commercial and institutional environments, and (ii) focused on lamp technology and occupancy detection. They often ignore user behaviour characteristics, which significantly influence energy consumption. Therefore, this study aims to identify alternative sensing techniques as part of a lighting control system that can energy-efficiently support user's behavioural needs in mixed-function residential spaces. An exploratory study investigated the optimal placement of a non-wearable radar sensor to detect an imitated user's breathing frequency at varying pre-set horizontal distance positions, and the sensor's performance was validated with a spirometer. The procedure measured a balloon's radar-detected distance, radar-detected breathing frequency, and spirometer-registered breathing frequency at each pre-set position. The radar sensor detected all simulated breathing frequencies with almost 100% data accuracy but was not comparable in detecting all distances. The radar offers a less intrusive short-range presence-sensing for homes, accurately detecting breathing frequencies in a contactless way between 0.2m to 0.8m. Further investigations are recommended to develop radar sensing that could predict lighting options based on user's objective feedback.

Due to the considerable increase of images in everyday life, many applications require a study on their similarity. The main challenge is to find a simple and efficient method to compare and classify image pairs into similar and dissimilar classes. This study presents a new method to image pairs comparison and classification based on the modeling of the Local Dissimilarity Map (LDM). The LDM is a tool for locally measuring the dissimilarity between two binary or grayscale images. It is a measure of dissimilarities based on a modified version of the Hausdorff distance, which allows quantifying locally the dissimilarities between images. This measure is completely without parameters and generic. The image pairs classification (2-class classification) method is structured as follows. First, a statistical model for the LDM is proposed. The model parameters, used as descriptors, are relevant to discriminate similar and dissimilar image pairs. Second, classifiers are applied to compute the classification scores (2-class classification problem). In addition, this approach is robust with respect to geometric transformations such as translation compared to the state-of-the-art similarity measures. Although the main objective of this paper is to apply our approach to image pairs classification, it is also performed on a classification with more than two classes (multi-class classification). Experiments on the well-known image data sets ^∗NIST and on old print data set prove that the proposed method produces comparable, even better results than the state-of-the-art methods in terms of accuracy and F(1) score.

Considering the unavailability of labeled data sets in remote sensing change detection, this letter presents a novel and low complexity unsupervised change detection method based on the combination of similarity and dissimilarity measures: mutual information (MI), disjoint information (DI), and local dissimilarity map (LDM). MI and DI are calculated on sliding windows with a step of 1 pixel for each pair of channels of both images. The resulting scalar values, weighted by q and m coefficients, are multiplied by the values of the center pixels of the windows weighted by p to remove the textures on images. The changes are detected using, respectively, the grayscale LDM and color LDM. A sliding window is then used on the color LDM and each pixel is characterized by a two-parameter Weibull distribution. Binarized change maps can be obtained by using a k-means clustering on the model parameters. Experiments on optical aerial image data set show that the proposed method produces comparable, even better results, to the state-of-the-art methods in terms of recall, precision, and F-measure.

This paper presents a new change detection technique for images taken from the sentinel-2 satellite between 2015 and 2018 in different regions of the world. These images are widely used in recent years for change detection. This technique is based on two dissimilarity measures: the Disjoint Information and the Local Dissimilarity Map. The disjoint information quantifies the dissimilarities between textures and the Local Dissimilarity Map those between structures of images. Firstly, the disjoint information is computed across the blocks of the RGB image channels and the value is multiplied by the center value of the pixel of each block. Secondly, the Local Dissimilarity Maps over the pre-processed channels and the average of the pixel values on the Local Dissimilarity Maps are computed. Finally, an extension of the Gaussian OTSU's threshold is used to detect changes in images. Experimental results on the well-known Onera Satellite Change Detection (OSCD) dataset show the effectiveness of our proposed method compared to the state-of-the-art deep learning methods.

This work aims at exploring a general framework embedding techniques from classifiers, Time–Frequency Distributions (TFD) and dissimilarity measures for epileptic seizures detection. The proposed approach consists firstly in computing dissimilarities between TFD of electroencephalogram (EEG) signals and secondly in using them to define a decision rule. Compared to the existing approaches, the proposed one uses entire TFD of EEG signals and does not require arbitrary feature extraction. Several dissimilarity measures and TFDs have been compared to select the most appropriate for EEG signals. Classifiers, such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Linear Discriminate Analysis (LDA) and k-Nearest Neighbours (k-NN), have been combined with the proposed approach. In order to evaluate the proposed approach, 13 different classification problems (including 2, 3 and 5-class) pertaining to five types of EEG signals have been used. The comparison between results obtained with the proposed approach and results reported in the literature with the same database of epileptic EEG signals demonstrates the effectiveness of this approach for seizure detection. Experimental results show that this approach has achieved highest accuracy in the most studied classification problems. A high value of 98% is achieved for the 5-class problem. Further, in most classification problems with 2 and 3-class, it also yields a satisfactory accuracy of approximately 100%. The robustness of the proposed approach is evaluated with the addition of noise to the EEG signals at various signal-to-noise ratios (SNRs). The experimental results show that this approach has a good classification accuracy at low SNRs.