Intentra --- Vision Core Intentra is an experimental vision-computing platform exploring how visual observations can be transformed into structured numerical representations of motion, spatial relationships, resonance, and inferred intent. Live application: https://intentra-vision-core.base44.app Project status: Experimental prototype / research platform. Features and mathematical models should be treated as developmental until independently tested and documented. Overview Intentra investigates a computational pipeline that moves from visual input to measurable state changes and higher-level interpretation: Observe --- acquire visual input from a supported camera or image source. Extract --- detect relevant visual features and numerical measurements. Represent --- convert observations into structured spatial and temporal data. Transform --- analyse numerical signals using Fourier-domain methods. Infer --- apply Bayesian reasoning to estimate the likelihood of candidate states or events. Map --- combine the outputs into Intentra's experimental resonance and intentionality representation. Present --- expose results through the application interface for inspection and evaluation. The aim is to make each stage inspectable, measurable, and replaceable rather than treating an inferred intention as a directly observed fact. Conceptual model Intentra explores a distinction between raw observation and interpretation. Observation: data directly obtained from a camera or input source. Feature: a measurable property extracted from the observation, such as position, displacement, direction, or temporal change. Resonance: an experimental representation of relationships or patterns among numerical features. Intent estimate: a model output inferred from available evidence. It is a hypothesis, not proof of a subject's internal mental state. Presential value: an experimental normalized score used in the project's conceptual mapping. Its meaning and calibration must be defined by the implementation and validated against test data. A useful high-level pipeline is: Input → Features → Numerical State → Signal Analysis → Probabilistic Inference → Resonance Mapping → Output Mathematical components Fourier analysis Fourier methods can be applied to numerical feature sequences to examine their frequency-domain structure. For a discrete sequence (x[n]), the Discrete Fourier Transform is: [ X[k] = \sum{=tex}_{n=0}^{N-1} x[n]e^{-i2\pi {=tex}kn/N} ] Potential uses include: identifying periodic motion; measuring changes in signal frequency and amplitude; comparing temporal patterns across feature channels; deriving frequency-domain descriptors for downstream analysis. Fourier analysis describes signal structure. It does not, by itself, determine intent. Bayesian inference Bayesian functions provide a probabilistic layer for updating estimates as evidence arrives: [ P(H\mid {=tex}E)=\frac{P(E\mid H)P(H)}{P(E)}{=tex} ] where (H) is a candidate hypothesis and (E) is observed evidence. In an implementation, hypotheses, priors, likelihood functions, and evidence variables should be explicitly defined. Probabilities should be calibrated and evaluated against labelled test cases. If the evidence model is not validated, outputs should be presented as experimental scores rather than reliable probabilities. Resonance and numerical mapping Intentra's resonance layer is a project-specific representation for relating numerical observations and transformed features. The mapping should document: the input variables and their units; normalization and scaling rules; the mathematical definition of resonance; thresholds and boundary conditions; missing-data and low-confidence behaviour; the interpretation of any aggregate score. Avoid treating a single score as a universal measure of intent. Keep component measurements and confidence information available for inspection. Current and planned capabilities The exact feature set depends on the deployed build. The following describes the project's intended development areas and should not be read as a claim that every item is already implemented. Camera-based visual input and feature extraction. Numerical representation of spatial displacement and temporal change. Fourier-domain analysis of numerical feature sequences. Bayesian estimation over explicitly defined candidate states. Resonance mapping and experimental presential scoring. Visual telemetry and inspection of intermediate outputs. Modular interfaces for future SDK and processor integration. Suggested architecture A modular implementation can separate the system into these components: Component Responsibility Input adapter Camera, image, or recorded-sequence input Vision processor Detection and extraction of visual features State builder Timestamped numerical and spatial state Signal analysis Fourier transforms and derived descriptors Inference engine Bayesian updates and confidence estimates Resonance mapper Project-defined numerical relationships Output and telemetry Visualizations, logs, and inspectable results Keep acquisition, analysis, inference, and presentation loosely coupled. This makes it possible to test each module independently and replace algorithms without rewriting the full application. Evaluation and validation Before using Intentra in consequential settings, establish a reproducible evaluation process: Create representative, consent-based test datasets. Record ground-truth labels where appropriate. Test each module independently before evaluating the complete pipeline. Compare Fourier features against known synthetic signals. Evaluate Bayesian estimates using calibration curves, Brier score, and held-out data. Measure vision performance across lighting, camera angles, occlusion, and movement speeds. Report latency, frame rate, dropped frames, and resource usage. Document failure cases and uncertainty, not only successful demonstrations. Do not infer a person's private thoughts, intentions, identity, or emotional state as fact from visual movement alone. Privacy, safety, and responsible use Obtain appropriate consent before capturing or processing identifiable people. Minimize collection and retention of images and video. Do not expose camera streams, credentials, API keys, or personal data in public logs. Provide clear notice when camera input is active. Treat model outputs as uncertain estimates and keep human review in the loop. Do not use experimental outputs as the sole basis for surveillance, employment, policing, military targeting, medical diagnosis, or other high-impact decisions. Development notes This README describes the research direction and conceptual architecture of Intentra Vision Core. For exact runtime requirements, dependencies, installation commands, API routes, and deployment instructions, consult the current application source and deployment configuration. Do not assume a local development command or API endpoint until it has been verified in the repository. Roadmap [ ] Document the deployed feature set and supported input formats. [ ] Publish the numerical feature schema and units. [ ] Specify Fourier transform windows, sampling assumptions, and output descriptors. [ ] Specify Bayesian hypotheses, priors, likelihoods, and calibration procedure. [ ] Formalize the resonance mapping and presential-score range. [ ] Add repeatable unit, integration, and performance tests. [ ] Document SDK interfaces and versioning. [ ] Publish benchmark results and known limitations. Project Name: Intentra --- Vision Core Live application: https://intentra-vision-core.base44.app Category: Experimental computer vision and intentional-computing research Intentra is an evolving research project. Mathematical descriptions in this document identify intended methods and conceptual components; they are not a claim of scientific validation or guaranteed performance.