Object Location Research Articles

Comparative Evaluation of NeRF Algorithms on Single Image Dataset for 3D Reconstruction

Abstract. The reconstruction of three-dimensional scenes from a single image represents a significant challenge in computer vision, particularly in the context of cultural heritage digitisation, where datasets may be limited or of poor quality. This paper addresses this challenge by conducting a study of the latest and most advanced algorithms for single-image 3D reconstruction, with a focus on applications in cultural heritage conservation. Exploiting different single-image datasets, the research evaluates the strengths and limitations of various artificial intelligence-based algorithms, in particular Neural Radiance Fields (NeRF), in reconstructing detailed 3D models from limited visual data. The study includes experiments on scenarios such as inaccessible or non-existent heritage sites, where traditional photogrammetric methods fail. The results demonstrate the effectiveness of NeRF-based approaches in producing accurate, high-resolution reconstructions suitable for visualisation and metric analysis. The results contribute to advancing the understanding of NeRF-based approaches in handling single-image inputs and offer insights for real-world applications such as object location and immersive content generation.

Integrated Data Processing Architecture Applied to Learning Objects Repository for Educational Robotics: Proposing Approach

Educational Robotics has emerged as a multidisciplinary field integrating Information and Communication Technologies, Engineering, and Artificial Intelligence areas in education. Furthermore, Educational Robotics offers an innovative approach to teaching and learning by incorporating the construction and programming of robots. However, the small number of platforms for sharing Educational Robotics Learning Objects with a large amount of well-described metadata poses a challenge for efficient search and retrieval of relevant educational content. This study presents an architecture for data processing and ingestion in the RepositORE system, a Learning Objects Repository for Educational Robotics that stores educational content and associated metadata based on the Dublin Core standard. Based on microservices architecture, the proposed system aims to facilitate the location and reuse of learning objects by adapting the RepositORE system to work as a client service. The theoretical framework explores the concepts of learning objects, repositories, and the significance of Educational Robotics in education, and reviews related works. The proposed architecture and system can potentially enhance the accessibility and discoverability of Educational Robotics Learning Objects, fostering effective teaching and learning experiences.

Less spatial exploration is associated with poorer spatial memory in midlife adults.

Despite its importance for navigation, very little is known about how the normal aging process affects spatial exploration behavior. We aimed to investigate: (1) how spatial exploration behavior may be altered early in the aging process, (2) the relationship between exploration behavior and subsequent spatial memory, and (3) whether exploration behavior can classify participants according to age. Fifty healthy young (aged 18-28) and 87 healthy midlife adults (aged 43-61) freely explored a desktop virtual maze, learning the locations of nine target objects. Various exploration behaviors (object visits, distance traveled, turns made, etc.) were measured. In the test phase, participants navigated from one target object to another without feedback, and their wayfinding success (% correct trials) was measured. In the exploration phase, midlife adults exhibited less exploration overall compared to young adults, and prioritized learning target object locations over maze layout. In the test phase, midlife adults exhibited less wayfinding success when compared to the young adults. Furthermore, following principal components analysis (PCA), regression analyses indicated that both exploration quantity and quality components were associated with wayfinding success in the midlife group, but not the young adults. Finally, we could classify participants according to age with similar accuracy using either their exploration behavior or wayfinding success scores. Our results aid in the understanding of how aging impacts spatial exploration, and encourages future investigations into how pathological aging may affect spatial exploration behavior.

The attentive reconstruction of objects facilitates robust object recognition.

Humans are extremely robust in our ability to perceive and recognize objects-we see faces in tea stains and can recognize friends on dark streets. Yet, neurocomputational models of primate object recognition have focused on the initial feed-forward pass of processing through the ventral stream and less on the top-down feedback that likely underlies robust object perception and recognition. Aligned with the generative approach, we propose that the visual system actively facilitates recognition by reconstructing the object hypothesized to be in the image. Top-down attention then uses this reconstruction as a template to bias feedforward processing to align with the most plausible object hypothesis. Building on auto-encoder neural networks, our model makes detailed hypotheses about the appearance and location of the candidate objects in the image by reconstructing a complete object representation from potentially incomplete visual input due to noise and occlusion. The model then leverages the best object reconstruction, measured by reconstruction error, to direct the bottom-up process of selectively routing low-level features, a top-down biasing that captures a core function of attention. We evaluated our model using the MNIST-C (handwritten digits under corruptions) and ImageNet-C (real-world objects under corruptions) datasets. Not only did our model achieve superior performance on these challenging tasks designed to approximate real-world noise and occlusion viewing conditions, but also better accounted for human behavioral reaction times and error patterns than a standard feedforward Convolutional Neural Network. Our model suggests that a complete understanding of object perception and recognition requires integrating top-down and attention feedback, which we propose is an object reconstruction.

Machine Learning for the Visually Impaired: Benchmarking Object Detection Models

This research paper benchmarks object detection models, a form of machine learning, to determine which algorithm would be most beneficial for the visually impaired to locate objects that are used in everyday life. As most benchmarking experiments test object detection models with a variety of objects, it is essential to test the models using images of more relevant objects to find the most suitable algorithm. The models are tested using still images from the COCO database. Pretrained models employing five of the most popular object detection algorithms are used to process the images and find each model’s detection accuracy. To simulate real life scenarios, these objects may be partially hidden or at a distance. For each image, the models return a list of detections providing the names, confidence rating, and location of each object detected. These results will be filtered to remove detections with low confidence ratings as well as detections of irrelevant objects. The remaining results are compared to the dataset of object names and locations provided by the COCO database to calculate the distance between the predicted object locations and the true location. The algorithms will be ranked based on the number of failed detections, the time taken to analyze each image, and the accuracy of each object’s determined location.

Object Location and Weight Sensing System based on the Reaction Force Ratio of the Load Cell

Object Location and Weight Sensing System based on the Reaction Force Ratio of the Load Cell

Memory loss and aberrant neurogenesis in mice exposed to patient anti-N-methyl-d-aspartate receptor antibodies

ObjectiveAnti-N-methyl-d-aspartate receptor (anti-NMDAR) encephalitis results in chronic epilepsy and permanent cognitive impairment. One of the possible causes of cognitive impairment in anti-NMDAR could be aberrant neurogenesis, an established contributor to memory loss in idiopathic drug-resistant epilepsy. We developed a mouse model of anti-NMDAR encephalitis and showed that mice exposed to patient anti-NMDAR antibodies for 2 weeks developed seizures and memory loss. In the present study, we assessed the delayed effects of patient-derived antibodies on cognitive phenotype and examined the corresponding changes in hippocampal neurogenesis. MethodsMonoclonal anti-NMDAR antibodies or control antibodies were continuously infused into the lateral ventricle of male C56BL/6J mice (8–12 weeks) via osmotic minipumps for 2 weeks. The motor and anxiety phenotypes were assessed using the open field paradigm, and hippocampal memory and learning were assessed using the object location, Y maze, and Barnes maze paradigms during weeks 1 and 3–4 of antibody washout. The numbers of newly matured granule neurons (Prox-1+) and immature progenitor cells (DCX+) as well as their spatial distribution within the hippocampus were assessed at these time points. Bromodeoxyuridine (BrdU, 50 mg/kg, i.p., daily) was injected on days 2–12 of the infusion, and proliferating cell immunoreactivity was compared in antibody-treated mice and control mice during week 4 of the washout. ResultsMice infused with anti-NMDAR antibodies demonstrated spatial memory impairment during week 1 of antibody washout (p = 0.02, t-test; n = 9–11). Histological analysis of hippocampal sections from these mice revealed an increased ectopic displacement of Prox-1+ cells in the dentate hilus compared to the control-antibody-treated mice (p = 0.01; t-test). Mice exposed to anti-NMDAR antibodies also had an impairment of spatial memory and learning during weeks 3–4 of antibody washout (object location: p = 0.009; t-test; Y maze: p = 0.006, t-test; Barnes maze: p = 0.008, ANOVA; n = 8–10). These mice showed increased ratios of the low proliferating (bright) to fast proliferating (faint) BrdU+ cell counts and decreased number of DCX+ cells in the hippocampal dentate gyrus (p = 0.006 and p = 0.04, respectively; t-tests) suggesting ectopic migration and delayed cell proliferation. SignificanceThese findings suggest that memory and learning impairments induced by patient anti-NMDAR antibodies are sustained upon removal of antibodies and are accompanied by aberrant hippocampal neurogenesis. Interventions directed at the manipulation of neuronal plasticity in patients with encephalitis and cognitive loss may be protective and therapeutically relevant.

Impaired synaptic plasticity and decreased glutamatergic neuron excitability induced by SIRT1/BDNF downregulation in the hippocampal CA1 region are involved in postoperative cognitive dysfunction

BackgroundPostoperative cognitive dysfunction (POCD) is a common complication after anesthesia/surgery, especially among elderly patients, and poses a significant threat to their postoperative quality of life and overall well-being. While it is widely accepted that elderly patients may experience POCD following anesthesia/surgery, the exact mechanism behind this phenomenon remains unclear. Several studies have indicated that the interaction between silent mating type information regulation 2 homologue 1 (SIRT1) and brain-derived neurotrophic factor (BDNF) is crucial in controlling cognitive function and is strongly linked to neurodegenerative disorders. Hence, this research aims to explore how SIRT1/BDNF impacts cognitive decline caused by anesthesia/surgery in aged mice.MethodsOpen field test (OFT) was used to determine whether anesthesia/surgery affected the motor ability of mice, while the postoperative cognitive function of 18 months old mice was evaluated with Novel object recognition test (NORT), Object location test (OLT) and Fear condition test (FC). The expressions of SIRT1 and other molecules were analyzed by western blot and immunofluorescence staining. The hippocampal synaptic plasticity was detected by Golgi staining and Long-term potentiation (LTP). The effects of SIRT1 and BDNF overexpression as well as chemogenetic activation of glutamatergic neurons in hippocampal CA1 region of 18 months old vesicular glutamate transporter 1 (VGLUT1) mice on POCD were further investigated.ResultsThe research results revealed that older mice exhibited cognitive impairment following intramedullary fixation of tibial fracture. Additionally, a notable decrease in the expression of SIRT1/BDNF and neuronal excitability in hippocampal CA1 glutamatergic neurons was observed. By increasing levels of SIRT1/BDNF or enhancing glutamatergic neuron excitability in the CA1 region, it was possible to effectively mitigate synaptic plasticity impairment and ameliorate postoperative cognitive dysfunction.ConclusionsThe decline in SIRT1/BDNF levels leading to changes in synaptic plasticity and neuronal excitability in older mice could be a significant factor contributing to cognitive impairment after anesthesia/surgery.Graphical

YOLIC: An efficient method for object localization and classification on edge devices

In the realm of Tiny AI, we introduce “You Only Look at Interested Cells” (YOLIC), an efficient method for object localization and classification on edge devices. Through seamlessly blending the strengths of semantic segmentation and object detection, YOLIC provides improved computational efficiency and precision compared to traditional methods. By adopting Cells of Interest for classification instead of individual pixels, YOLIC encapsulates relevant information, reduces computational load, and enables rough object shape inference. Importantly, the need for bounding box regression is obviated, as YOLIC capitalizes on the predetermined cell configuration that provides information about potential object location, size, and shape. To tackle the issue of single-label classification limitations, a multi-label classification approach is applied to each cell for effectively recognizing overlapping or closely situated objects. This paper presents extensive experiments on multiple datasets to demonstrate that YOLIC achieves detection performance comparable to the state-of-the-art YOLO algorithms while surpassing in speed, exceeding 30fps on a Raspberry Pi 4B CPU.

Inflammation subsequent to mild iron excess differentially alters regional brain iron metabolism, oxidation and neuroinflammation status in mice.

Iron dyshomeostasis and neuroinflammation, characteristic features of the aged brain, and exacerbated in neurodegenerative disease, may induce oxidative stress-mediated neurodegeneration. In this study, the effects of potential priming with mild systemic iron injections on subsequent lipopolysaccharide (LPS)-induced inflammation in adult C57Bl/6J mice were examined. After cognitive testing, regional brain tissues were dissected for iron (metal) measurements by total reflection X-ray fluorescence and synchrotron radiation X-Ray fluorescence-based elemental mapping; and iron regulatory, ferroptosis-related, and glia-specific protein analysis, and lipid peroxidation by western blotting. Microglial morphology and astrogliosis were assessed by immunohistochemistry. Iron only treatment enhanced cognitive performance on the novel object location task compared with iron priming and subsequent LPS-induced inflammation. LPS-induced inflammation, with or without iron treatment, attenuated hippocampal heme oxygenase-1 and augmented 4-hydroxynonenal levels. Conversely, in the cortex, elevated ferritin light chain and xCT (light chain of System Xc-) were observed in response to LPS-induced inflammation, without and with iron-priming. Increased microglial branch/process lengths and astrocyte immunoreactivity were also increased by combined iron and LPS in both the hippocampus and cortex. Here, we demonstrate iron priming and subsequent LPS-induced inflammation led to iron dyshomeostasis, compromised antioxidant function, increased lipid peroxidation and altered neuroinflammatory state in a brain region-dependent manner.

Simulation Model Design of VHF Omni-Directional Range (VOR) Based on Microcontroller

The process of giving directional information to assist airplanes in making an accurate landing at the airport of destination is known as aviation navigation. The VHF Omnidirectional Range is a crucial navigation tool for guiding planes to the airport (VOR). Since it costs a lot of money to learn how to fly an airplane, a VOR simulator was developed that can mimic the movement of an aircraft passing through the VOR. The simulation that was produced is a prototype that sends and receives signals to the aircraft utilizing an antenna and microcontroller as supporting hardware. The VOR/DME flight navigation system is constructed in this study using mathematical modeling; a formulation representing the essential features of the system is expressed as a set of connected variables. The ESP32 module that powers the system serves as a DME by sending out Bluetooth radio signals. where one ESP32 module serves as a DME object (moving DME) and three ESP32 modules serve as DME stations (ground DME). Information on the distance between the DME station and the DME object will be communicated using the MQTT protocol, and this data will be processed using the trilateration method to predict the location and movement of the DME object. The measurement accuracy at DME station 1, DME station 2, and DME station 3 are 99.52%, 99.92%, and 99.98% respectively. The enhanced capabilities to estimate the position of objects observed from different directions or omni-directionally on a two-dimensional scale are made possible by the performance results of combining the performance of three ESP32 devices as Distance Measurement Equipment (DME).

Enhancing adversarial robustness for deep metric learning via neural discrete adversarial training

Due to the security concerns arising from adversarial vulnerability in deep metric learning models, it is essential to enhance their adversarial robustness for secure neural network software development. Existing defense strategies utilize adversarial triplets to enhance adversarial robustness but sacrifice benign performance. This paper proposes a novel framework for enhancing adversarial robustness and maintaining benign performance by introducing the concept of Neural Discrete Adversarial Training (NDAT) for deep metric learning. NDAT employs VQGAN to transform the adversarial triplets into discrete inputs and then minimizes metric loss function on discrete adversarial triplets. NDAT aligns discrete adversarial examples more closely with clean samples, significantly reducing distribution deviation from their clean counterparts. Moreover, the visual explanations reveal that NDAT maintains consistent attention maps between benign and adversarial triplets and concentrates on structure details and object location perturbations. To demonstrate the effectiveness of our approach, we combine NDAT with popular adversarial methods under various perturbation iterations and intensities. Experiment evaluations on three benchmark databases illustrate that our proposed framework for deep metric learning significantly outperforms state-of-the-art defense approaches in terms of both adversarial robustness and benign performance.

Accurate object localization facilitates automatic esophagus segmentation in deep learning

BackgroundCurrently, automatic esophagus segmentation remains a challenging task due to its small size, low contrast, and large shape variation. We aimed to improve the performance of esophagus segmentation in deep learning by applying a strategy that involves locating the object first and then performing the segmentation task.MethodsA total of 100 cases with thoracic computed tomography scans from two publicly available datasets were used in this study. A modified CenterNet, an object location network, was employed to locate the center of the esophagus for each slice. Subsequently, the 3D U-net and 2D U-net_coarse models were trained to segment the esophagus based on the predicted object center. A 2D U-net_fine model was trained based on the updated object center according to the 3D U-net model. The dice similarity coefficient and the 95% Hausdorff distance were used as quantitative evaluation indexes for the delineation performance. The characteristics of the automatically delineated esophageal contours by the 2D U-net and 3D U-net models were summarized. Additionally, the impact of the accuracy of object localization on the delineation performance was analyzed. Finally, the delineation performance in different segments of the esophagus was also summarized.ResultsThe mean dice coefficient of the 3D U-net, 2D U-net_coarse, and 2D U-net_fine models were 0.77, 0.81, and 0.82, respectively. The 95% Hausdorff distance for the above models was 6.55, 3.57, and 3.76, respectively. Compared with the 2D U-net, the 3D U-net has a lower incidence of delineating wrong objects and a higher incidence of missing objects. After using the fine object center, the average dice coefficient was improved by 5.5% in the cases with a dice coefficient less than 0.75, while that value was only 0.3% in the cases with a dice coefficient greater than 0.75. The dice coefficients were lower for the esophagus between the orifice of the inferior and the pulmonary bifurcation compared with the other regions.ConclusionThe 3D U-net model tended to delineate fewer incorrect objects but also miss more objects. Two-stage strategy with accurate object location could enhance the robustness of the segmentation model and significantly improve the esophageal delineation performance, especially for cases with poor delineation results.

DeforT: Deformable transformer for visual tracking

Most trackers formulate visual tracking as common classification and regression (i.e., bounding box regression) tasks. Correlation features that are computed through depth-wise convolution or channel-wise multiplication operations are input into both the classification and regression branches for inference. However, this matching computation with the linear correlation method tends to lose semantic features and obtain only a local optimum. Moreover, these trackers use an unreliable ranking based on the classification score and the intersection over union (IoU) loss for the regression training, thus degrading the tracking performance. In this paper, we introduce a deformable transformer model, which effectively computes the correlation features of the training and search sets. A new loss called the quality-aware focal loss (QAFL) is used to train the classification network; it efficiently alleviates the inconsistency between the classification and localization quality predictions. We use a new regression loss called α-GIoU to train the regression network, and it effectively improves localization accuracy. To further improve the tracker’s robustness, the candidate object location is predicted by using a combination of online learning scores with a transformer-assisted framework and classification scores. An extensive experiment on six testing datasets demonstrates the effectiveness of our method. In particular, the proposed method attains a success score of 71.7% on the OTB-2015 dataset and an AUC score of 67.3% on the NFS30 dataset, respectively.

Paradoxical boosting of weak and strong spatial memories by hippocampal dopamine uncaging.

The ability to remember changes in the surroundings is fundamental for daily life. It has been proposed that novel events producing dopamine release in the hippocampal CA1 region could modulate spatial memory formation. However, the role of hippocampal dopamine increase on weak or strong spatial memories remains unclear. We show that male mice exploring two objects located in a familiar environment for 5 minutes created a short-term memory (weak) that cannot be retrieved one day later, whereas 10 minutes exploration created a long-term memory (strong) that can be retrieved one day later. Remarkably, hippocampal dopamine elevation during the encoding of weak object location memories (OLMs) allowed their retrieval one day later but dopamine elevation during the encoding of strong OLMs promoted the preference for a familiar object location over a novel object location after 24 hours. Moreover, dopamine uncaging after the encoding of OLMs did not have effect on weak memories whereas on strong memories diminished the exploration of the novel object location. Additionally, hippocampal dopamine elevation during the retrieval of OLMs did not allow the recovery of weak memories and did not affect the retrieval of strong memory traces. Finally, dopamine elevation increased hippocampal theta oscillations, indicating that dopamine promotes the recurrent activation of specific groups of neurons. Our experiments demonstrate that hippocampal dopaminergic modulation during the encoding of OLMs depends on memory strength indicating that hyperdopaminergic levels that enhance weak experiences could compromise the normal storage of strong memories.Significance statement Increased levels of dopamine have been related to cognitive enhancement. Hippocampal dopamine elevation caused by novelty exposure has been proposed as a strategy to enhance memory based on the observation that surprising events create flashbulb memories that are remembered for long time. However, hyperdopaminergic levels could also underlie maladaptive memories, such as non-desired preservation of traumatic experiences. Our experiments show that dopamine elevation in the dorsal hippocampus during the encoding of spatial memories has paradoxical effects, while the enhancement of weak memories allows their retrieval, the dopaminergic modulation of strong memories limits the ability to modify preexisting spatial memories by changes in the environment. We conclude that cognitive enhancement through dopamine boosting must consider diverse aspects of memory formation.

CenterNet++ for Object Detection.

There are two mainstream approaches for object detection: top-down and bottom-up. The state-of-the-art approaches are mainly top-down methods. In this paper, we demonstrate that bottom-up approaches show competitive performance compared with top-down approaches and have higher recall rates. Our approach, named CenterNet, detects each object as a triplet of keypoints (top-left and bottom-right corners and the center keypoint). We first group the corners according to some designed cues and confirm the object locations based on the center keypoints. The corner keypoints allow the approach to detect objects of various scales and shapes and the center keypoint reduces the confusion introduced by a large number of false-positive proposals. Our approach is an anchor-free detector because it does not need to define explicit anchor boxes. We adapt our approach to backbones with different structures, including 'hourglass'-like networks and 'pyramid'-like networks, which detect objects in single-resolution and multi-resolution feature maps, respectively. On the MS-COCO dataset, CenterNet with Res2Net-101 and Swin-Transformer achieve average precisions (APs) of 53.7% and 57.1%, respectively, outperforming all existing bottom-up detectors and achieving state-of-the-art performance. We also design a real-time CenterNet model, which achieves a good trade-off between accuracy and speed, with an AP of 43.6% at 30.5 frames per second (FPS).

Decision framework for public-private partnership in agricultural commodities

Public-private partnerships are critical to advancing sustainability in the agricultural sector and increasing global food security. Partnerships are a means of increasing productivity and encouraging growth in the agricultural sector. The Partnership encourages private sector participation to reduce risks that occur in the agricultural sector. The aim of this research is to understand how supply chains are configured, to identify drivers of supply chain partnerships and to synthesize partnership strategies. Currently, agricultural areas in East Java are trying to build sustainable partnerships to improve farmers’ abilities in managing the agricultural sector. By using data from surveys and observations of 50 private companies and 50 farmers spread across the research object locations, it was found that there was quite a significant interaction between intensive coordination, partnership quality, quantity and quality of partners. The mechanisms used in partnerships can encourage sustainable agricultural management. It is hoped that the implementation of these findings can encourage partnerships between the private sector and the public sector to achieve significant development. It is hoped that this partnership can play an important role in addressing agricultural problems. Partnerships are formed to create shared values in the form of an institutional framework to address the root of problems.

Modeling the Thermoelastic Sample Response for Subdiffraction Infrared Spectroscopic Imaging.

There is significant and increasing interest in using the photothermal effect to record infrared (IR) absorption spectra localized to volumes that are considerably smaller than the wavelength of excitation, i.e., subdiffraction imaging. As opposed to conventional IR microscopy, in which absorption and scattering of the illuminating light is measured, subdiffraction imaging can be achieved through detection of the sample's thermal response to IR absorption-induced heating. While this relationship has been examined by a variety of coarse-grained models, a generalized analysis of the dependence of temperature and surface deformation arising from an absorber below the surface has not been reported. Here, we present an analytical model to understand a sample's thermoelastic response in photothermal measurements. The model shows important dependence of the ability to record subdiffraction data on modulation frequency of exciting light, limitations imposed by optical sensing, and the potential to discern location of objects ultimately limited by noise and sharpness of the detecting mechanism. This foundational analysis should allow for better modeling, understanding, and harnessing of the relationship between absorption and sample response that underlies IR photothermal measurements.

Gaze patterns reflect the retrieval and selection of memories in a context-dependent object location retrieval task

Selective retrieval of context-relevant memories is critical for animal survival. A behavioral index that captures its dynamic nature in real time is necessary to investigate this retrieval process. Here, we found a bias in eye gaze towards the locations previously associated with individual objects during retrieval. Participants learned two locations associated with each visual object and recalled one of them indicated by a contextual cue in the following days. Before the contextual cue presentation, participants often gazed at both locations associated with the given object on the background screen (look-at-both), and the frequency of look-at-both gaze pattern increased as learning progressed. Following the cue presentation, their gaze shifted toward the context-appropriate location. Interestingly, participants showed a higher accuracy of memory retrieval in trials where they gazed at both object-associated locations, implying functional advantage of the look-at-both gaze patterns. Our findings indicate that naturalistic eye movements reflect the dynamic process of memory retrieval and selection, highlighting the potential of eye gaze as an indicator for studying these cognitive processes.

Limited information-processing capacity in vision explains number psychophysics.

Humans and other animals are able to perceive and represent a number of objects present in a scene, a core cognitive ability thought to underlie the development of mathematics. However, the perceptual mechanisms that underpin this capacity remain poorly understood. Here, we show that our visual sense of number derives from a visual system designed to efficiently encode the location of objects in scenes. Using a mathematical model, we demonstrate that an efficient but information-limited encoding of objects' locations can explain many key aspects of number psychophysics, including subitizing, Weber's law, underestimation, and effects of exposure time. In two experiments (N = 100 each), we find that this model of visual encoding captures human performance in both a change-localization task and a number estimation task. In a third experiment (N = 100), we find that individual differences in change-localization performance are highly predictive of differences in number estimation, both in terms of overall performance and inferred model parameters, with participants having numerically indistinguishable inferred information capacities across tasks. Our results therefore indicate that key psychophysical features of numerical cognition do not arise from separate modules or capacities specific to number, but rather as by-products of lower level constraints on perception. (PsycInfo Database Record (c) 2024 APA, all rights reserved).